Overview

Dataset statistics

Number of variables131
Number of observations129446
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory129.4 MiB
Average record size in memory1.0 KiB

Variable types

Numeric15
Categorical111
Text5

Alerts

cont1 is highly overall correlated with cont6 and 5 other fieldsHigh correlation
cont3 is highly overall correlated with cat71High correlation
cont4 is highly overall correlated with cont8High correlation
cont6 is highly overall correlated with cont1 and 9 other fieldsHigh correlation
cont7 is highly overall correlated with cont6 and 4 other fieldsHigh correlation
cont8 is highly overall correlated with cont4High correlation
cont9 is highly overall correlated with cont1 and 9 other fieldsHigh correlation
cont10 is highly overall correlated with cont1 and 7 other fieldsHigh correlation
cont11 is highly overall correlated with cont1 and 5 other fieldsHigh correlation
cont12 is highly overall correlated with cont1 and 5 other fieldsHigh correlation
cont13 is highly overall correlated with cont6 and 7 other fieldsHigh correlation
cat1 is highly overall correlated with cat100High correlation
cat2 is highly overall correlated with cat9 and 1 other fieldsHigh correlation
cat3 is highly overall correlated with cat16 and 2 other fieldsHigh correlation
cat4 is highly overall correlated with cat23 and 2 other fieldsHigh correlation
cat5 is highly overall correlated with cat36 and 2 other fieldsHigh correlation
cat6 is highly overall correlated with cat50 and 2 other fieldsHigh correlation
cat7 is highly overall correlated with cat57 and 1 other fieldsHigh correlation
cat8 is highly overall correlated with cat66 and 1 other fieldsHigh correlation
cat9 is highly overall correlated with cat2 and 1 other fieldsHigh correlation
cat10 is highly overall correlated with cat12 and 1 other fieldsHigh correlation
cat11 is highly overall correlated with cat101High correlation
cat12 is highly overall correlated with cat10 and 1 other fieldsHigh correlation
cat13 is highly overall correlated with cat101High correlation
cat14 is highly overall correlated with cat76High correlation
cat16 is highly overall correlated with cat3 and 2 other fieldsHigh correlation
cat17 is highly overall correlated with cat90High correlation
cat23 is highly overall correlated with cat4 and 1 other fieldsHigh correlation
cat24 is highly overall correlated with cat28High correlation
cat25 is highly overall correlated with cat111High correlation
cat26 is highly overall correlated with cat111High correlation
cat27 is highly overall correlated with cat4 and 1 other fieldsHigh correlation
cat28 is highly overall correlated with cat24High correlation
cat36 is highly overall correlated with cat5 and 1 other fieldsHigh correlation
cat37 is highly overall correlated with cat5 and 1 other fieldsHigh correlation
cat38 is highly overall correlated with cat103High correlation
cat40 is highly overall correlated with cat41High correlation
cat41 is highly overall correlated with cat40High correlation
cat44 is highly overall correlated with cat103High correlation
cat49 is highly overall correlated with cat114High correlation
cat50 is highly overall correlated with cat6 and 2 other fieldsHigh correlation
cat52 is highly overall correlated with cat114High correlation
cat53 is highly overall correlated with cat114High correlation
cat57 is highly overall correlated with cat7 and 1 other fieldsHigh correlation
cat65 is highly overall correlated with cat102High correlation
cat66 is highly overall correlated with cat8 and 1 other fieldsHigh correlation
cat71 is highly overall correlated with cont3 and 1 other fieldsHigh correlation
cat73 is highly overall correlated with cat74 and 3 other fieldsHigh correlation
cat74 is highly overall correlated with cat73 and 3 other fieldsHigh correlation
cat76 is highly overall correlated with cat14 and 6 other fieldsHigh correlation
cat77 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat78 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat80 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat81 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat85 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat86 is highly overall correlated with cont9 and 7 other fieldsHigh correlation
cat87 is highly overall correlated with cat3 and 7 other fieldsHigh correlation
cat88 is highly overall correlated with cat98 and 1 other fieldsHigh correlation
cat89 is highly overall correlated with cat7 and 1 other fieldsHigh correlation
cat90 is highly overall correlated with cat3 and 2 other fieldsHigh correlation
cat91 is highly overall correlated with cat73 and 3 other fieldsHigh correlation
cat92 is highly overall correlated with cat73 and 2 other fieldsHigh correlation
cat95 is highly overall correlated with cont13 and 4 other fieldsHigh correlation
cat96 is highly overall correlated with cat71High correlation
cat97 is highly overall correlated with cont1 and 7 other fieldsHigh correlation
cat98 is highly overall correlated with cont9 and 8 other fieldsHigh correlation
cat99 is highly overall correlated with cat86 and 3 other fieldsHigh correlation
cat100 is highly overall correlated with cat1 and 5 other fieldsHigh correlation
cat101 is highly overall correlated with cat2 and 5 other fieldsHigh correlation
cat102 is highly overall correlated with cat8 and 2 other fieldsHigh correlation
cat103 is highly overall correlated with cat5 and 4 other fieldsHigh correlation
cat104 is highly overall correlated with cont6 and 1 other fieldsHigh correlation
cat106 is highly overall correlated with cont6 and 1 other fieldsHigh correlation
cat107 is highly overall correlated with cat86 and 3 other fieldsHigh correlation
cat108 is highly overall correlated with cont13 and 6 other fieldsHigh correlation
cat111 is highly overall correlated with cat4 and 4 other fieldsHigh correlation
cat114 is highly overall correlated with cat6 and 4 other fieldsHigh correlation
cat115 is highly overall correlated with cont13 and 3 other fieldsHigh correlation
cat3 is highly imbalanced (69.6%)Imbalance
cat7 is highly imbalanced (83.7%)Imbalance
cat8 is highly imbalanced (67.6%)Imbalance
cat11 is highly imbalanced (51.8%)Imbalance
cat13 is highly imbalanced (52.1%)Imbalance
cat14 is highly imbalanced (90.2%)Imbalance
cat15 is highly imbalanced (99.8%)Imbalance
cat16 is highly imbalanced (78.5%)Imbalance
cat17 is highly imbalanced (94.0%)Imbalance
cat18 is highly imbalanced (95.3%)Imbalance
cat19 is highly imbalanced (92.3%)Imbalance
cat20 is highly imbalanced (99.0%)Imbalance
cat21 is highly imbalanced (97.9%)Imbalance
cat22 is highly imbalanced (99.7%)Imbalance
cat24 is highly imbalanced (79.2%)Imbalance
cat25 is highly imbalanced (54.2%)Imbalance
cat26 is highly imbalanced (67.5%)Imbalance
cat27 is highly imbalanced (51.2%)Imbalance
cat28 is highly imbalanced (76.6%)Imbalance
cat29 is highly imbalanced (86.2%)Imbalance
cat30 is highly imbalanced (86.8%)Imbalance
cat31 is highly imbalanced (81.7%)Imbalance
cat32 is highly imbalanced (94.3%)Imbalance
cat33 is highly imbalanced (95.4%)Imbalance
cat34 is highly imbalanced (97.2%)Imbalance
cat35 is highly imbalanced (98.8%)Imbalance
cat38 is highly imbalanced (52.5%)Imbalance
cat39 is highly imbalanced (82.1%)Imbalance
cat40 is highly imbalanced (74.2%)Imbalance
cat41 is highly imbalanced (76.9%)Imbalance
cat42 is highly imbalanced (92.2%)Imbalance
cat43 is highly imbalanced (84.8%)Imbalance
cat44 is highly imbalanced (58.2%)Imbalance
cat45 is highly imbalanced (84.1%)Imbalance
cat46 is highly imbalanced (95.8%)Imbalance
cat47 is highly imbalanced (96.3%)Imbalance
cat48 is highly imbalanced (98.5%)Imbalance
cat49 is highly imbalanced (72.1%)Imbalance
cat51 is highly imbalanced (94.6%)Imbalance
cat52 is highly imbalanced (73.1%)Imbalance
cat53 is highly imbalanced (59.5%)Imbalance
cat54 is highly imbalanced (83.9%)Imbalance
cat55 is highly imbalanced (99.1%)Imbalance
cat56 is highly imbalanced (99.0%)Imbalance
cat57 is highly imbalanced (88.3%)Imbalance
cat58 is highly imbalanced (98.6%)Imbalance
cat59 is highly imbalanced (98.2%)Imbalance
cat60 is highly imbalanced (97.8%)Imbalance
cat61 is highly imbalanced (96.1%)Imbalance
cat62 is highly imbalanced (99.8%)Imbalance
cat63 is highly imbalanced (99.5%)Imbalance
cat64 is highly imbalanced (99.7%)Imbalance
cat65 is highly imbalanced (90.5%)Imbalance
cat66 is highly imbalanced (73.6%)Imbalance
cat67 is highly imbalanced (96.7%)Imbalance
cat68 is highly imbalanced (99.0%)Imbalance
cat69 is highly imbalanced (98.3%)Imbalance
cat70 is highly imbalanced (99.8%)Imbalance
cat71 is highly imbalanced (71.1%)Imbalance
cat73 is highly imbalanced (56.5%)Imbalance
cat74 is highly imbalanced (91.0%)Imbalance
cat75 is highly imbalanced (57.0%)Imbalance
cat76 is highly imbalanced (84.3%)Imbalance
cat77 is highly imbalanced (97.6%)Imbalance
cat78 is highly imbalanced (95.3%)Imbalance
cat79 is highly imbalanced (56.0%)Imbalance
cat80 is highly imbalanced (51.5%)Imbalance
cat81 is highly imbalanced (56.9%)Imbalance
cat84 is highly imbalanced (61.2%)Imbalance
cat85 is highly imbalanced (94.2%)Imbalance
cat87 is highly imbalanced (67.8%)Imbalance
cat88 is highly imbalanced (75.6%)Imbalance
cat89 is highly imbalanced (94.3%)Imbalance
cat90 is highly imbalanced (87.3%)Imbalance
cat92 is highly imbalanced (68.0%)Imbalance
cat93 is highly imbalanced (65.0%)Imbalance
cat94 is highly imbalanced (55.2%)Imbalance
cat96 is highly imbalanced (84.3%)Imbalance
cat99 is highly imbalanced (50.8%)Imbalance
cat102 is highly imbalanced (85.4%)Imbalance
cat103 is highly imbalanced (58.6%)Imbalance
cat111 is highly imbalanced (65.0%)Imbalance
cat114 is highly imbalanced (61.0%)Imbalance

Reproduction

Analysis started2023-09-24 21:56:44.140952
Analysis finished2023-09-24 22:00:37.329124
Duration3 minutes and 53.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct125546
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302633.82
Minimum4
Maximum587634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:00:37.494269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile29973
Q1151198.25
median303543.5
Q3455525.5
95-th percentile572535
Maximum587634
Range587630
Interquartile range (IQR)304327.25

Descriptive statistics

Standard deviation174427.63
Coefficient of variation (CV)0.5763653
Kurtosis-1.2253683
Mean302633.82
Median Absolute Deviation (MAD)152157.5
Skewness-0.019371807
Sum3.9174738 × 1010
Variance3.0424999 × 1010
MonotonicityNot monotonic
2023-09-24T16:00:37.732705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
587634 2
 
< 0.1%
575223 2
 
< 0.1%
575365 2
 
< 0.1%
575363 2
 
< 0.1%
575359 2
 
< 0.1%
575357 2
 
< 0.1%
575341 2
 
< 0.1%
575324 2
 
< 0.1%
575323 2
 
< 0.1%
575317 2
 
< 0.1%
Other values (125536) 129426
> 99.9%
ValueCountFrequency (%)
4 1
< 0.1%
6 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
15 1
< 0.1%
17 1
< 0.1%
21 1
< 0.1%
28 1
< 0.1%
32 1
< 0.1%
43 1
< 0.1%
ValueCountFrequency (%)
587634 2
< 0.1%
587629 2
< 0.1%
587627 2
< 0.1%
587621 2
< 0.1%
587617 2
< 0.1%
587613 2
< 0.1%
587610 2
< 0.1%
587596 2
< 0.1%
587587 2
< 0.1%
587583 2
< 0.1%

cat1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
97008 
B
32438 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A 97008
74.9%
B 32438
 
25.1%

Length

2023-09-24T16:00:37.939191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:38.105922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 97008
74.9%
b 32438
 
25.1%

Most occurring characters

ValueCountFrequency (%)
A 97008
74.9%
B 32438
 
25.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 97008
74.9%
B 32438
 
25.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 97008
74.9%
B 32438
 
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 97008
74.9%
B 32438
 
25.1%

cat2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
73448 
B
55998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 73448
56.7%
B 55998
43.3%

Length

2023-09-24T16:00:38.564353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:38.727267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 73448
56.7%
b 55998
43.3%

Most occurring characters

ValueCountFrequency (%)
A 73448
56.7%
B 55998
43.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 73448
56.7%
B 55998
43.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 73448
56.7%
B 55998
43.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 73448
56.7%
B 55998
43.3%

cat3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
122432 
B
 
7014

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 122432
94.6%
B 7014
 
5.4%

Length

2023-09-24T16:00:38.911088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:39.082690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 122432
94.6%
b 7014
 
5.4%

Most occurring characters

ValueCountFrequency (%)
A 122432
94.6%
B 7014
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 122432
94.6%
B 7014
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 122432
94.6%
B 7014
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 122432
94.6%
B 7014
 
5.4%

cat4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
88720 
B
40726 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 88720
68.5%
B 40726
31.5%

Length

2023-09-24T16:00:39.279495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:39.443916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 88720
68.5%
b 40726
31.5%

Most occurring characters

ValueCountFrequency (%)
A 88720
68.5%
B 40726
31.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 88720
68.5%
B 40726
31.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 88720
68.5%
B 40726
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 88720
68.5%
B 40726
31.5%

cat5
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
84753 
B
44693 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 84753
65.5%
B 44693
34.5%

Length

2023-09-24T16:00:39.634339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:39.801467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 84753
65.5%
b 44693
34.5%

Most occurring characters

ValueCountFrequency (%)
A 84753
65.5%
B 44693
34.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 84753
65.5%
B 44693
34.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 84753
65.5%
B 44693
34.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 84753
65.5%
B 44693
34.5%

cat6
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
90742 
B
38704 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A 90742
70.1%
B 38704
29.9%

Length

2023-09-24T16:00:39.989841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:40.165185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 90742
70.1%
b 38704
29.9%

Most occurring characters

ValueCountFrequency (%)
A 90742
70.1%
B 38704
29.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 90742
70.1%
B 38704
29.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 90742
70.1%
B 38704
29.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 90742
70.1%
B 38704
29.9%

cat7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
126349 
B
 
3097

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 126349
97.6%
B 3097
 
2.4%

Length

2023-09-24T16:00:40.363233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:40.530741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 126349
97.6%
b 3097
 
2.4%

Most occurring characters

ValueCountFrequency (%)
A 126349
97.6%
B 3097
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 126349
97.6%
B 3097
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 126349
97.6%
B 3097
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 126349
97.6%
B 3097
 
2.4%

cat8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
121786 
B
 
7660

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 121786
94.1%
B 7660
 
5.9%

Length

2023-09-24T16:00:40.716426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:40.884588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 121786
94.1%
b 7660
 
5.9%

Most occurring characters

ValueCountFrequency (%)
A 121786
94.1%
B 7660
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 121786
94.1%
B 7660
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 121786
94.1%
B 7660
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 121786
94.1%
B 7660
 
5.9%

cat9
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
77911 
B
51535 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 77911
60.2%
B 51535
39.8%

Length

2023-09-24T16:00:41.081860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:41.259325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 77911
60.2%
b 51535
39.8%

Most occurring characters

ValueCountFrequency (%)
A 77911
60.2%
B 51535
39.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 77911
60.2%
B 51535
39.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 77911
60.2%
B 51535
39.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 77911
60.2%
B 51535
39.8%

cat10
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
110272 
B
19174 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 110272
85.2%
B 19174
 
14.8%

Length

2023-09-24T16:00:41.451274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:41.621330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 110272
85.2%
b 19174
 
14.8%

Most occurring characters

ValueCountFrequency (%)
A 110272
85.2%
B 19174
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 110272
85.2%
B 19174
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 110272
85.2%
B 19174
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 110272
85.2%
B 19174
 
14.8%

cat11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
115985 
B
13461 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 115985
89.6%
B 13461
 
10.4%

Length

2023-09-24T16:00:41.816286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:41.994218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 115985
89.6%
b 13461
 
10.4%

Most occurring characters

ValueCountFrequency (%)
A 115985
89.6%
B 13461
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 115985
89.6%
B 13461
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 115985
89.6%
B 13461
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 115985
89.6%
B 13461
 
10.4%

cat12
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
110116 
B
19330 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 110116
85.1%
B 19330
 
14.9%

Length

2023-09-24T16:00:42.177005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:42.343161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 110116
85.1%
b 19330
 
14.9%

Most occurring characters

ValueCountFrequency (%)
A 110116
85.1%
B 19330
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 110116
85.1%
B 19330
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 110116
85.1%
B 19330
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 110116
85.1%
B 19330
 
14.9%

cat13
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
116110 
B
13336 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 116110
89.7%
B 13336
 
10.3%

Length

2023-09-24T16:00:42.529025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:42.704781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 116110
89.7%
b 13336
 
10.3%

Most occurring characters

ValueCountFrequency (%)
A 116110
89.7%
B 13336
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 116110
89.7%
B 13336
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 116110
89.7%
B 13336
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 116110
89.7%
B 13336
 
10.3%

cat14
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
127803 
B
 
1643

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 127803
98.7%
B 1643
 
1.3%

Length

2023-09-24T16:00:42.917313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:43.091416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 127803
98.7%
b 1643
 
1.3%

Most occurring characters

ValueCountFrequency (%)
A 127803
98.7%
B 1643
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 127803
98.7%
B 1643
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 127803
98.7%
B 1643
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 127803
98.7%
B 1643
 
1.3%

cat15
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129423 
B
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129423
> 99.9%
B 23
 
< 0.1%

Length

2023-09-24T16:00:43.282739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:43.453405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129423
> 99.9%
b 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 129423
> 99.9%
B 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129423
> 99.9%
B 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129423
> 99.9%
B 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129423
> 99.9%
B 23
 
< 0.1%

cat16
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
125027 
B
 
4419

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 125027
96.6%
B 4419
 
3.4%

Length

2023-09-24T16:00:43.641465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:43.809808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 125027
96.6%
b 4419
 
3.4%

Most occurring characters

ValueCountFrequency (%)
A 125027
96.6%
B 4419
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 125027
96.6%
B 4419
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 125027
96.6%
B 4419
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 125027
96.6%
B 4419
 
3.4%

cat17
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128542 
B
 
904

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128542
99.3%
B 904
 
0.7%

Length

2023-09-24T16:00:43.998221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:44.164465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128542
99.3%
b 904
 
0.7%

Most occurring characters

ValueCountFrequency (%)
A 128542
99.3%
B 904
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128542
99.3%
B 904
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128542
99.3%
B 904
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128542
99.3%
B 904
 
0.7%

cat18
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128779 
B
 
667

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128779
99.5%
B 667
 
0.5%

Length

2023-09-24T16:00:44.373133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:44.553852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128779
99.5%
b 667
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 128779
99.5%
B 667
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128779
99.5%
B 667
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128779
99.5%
B 667
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128779
99.5%
B 667
 
0.5%

cat19
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128231 
B
 
1215

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128231
99.1%
B 1215
 
0.9%

Length

2023-09-24T16:00:44.752323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:44.922639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128231
99.1%
b 1215
 
0.9%

Most occurring characters

ValueCountFrequency (%)
A 128231
99.1%
B 1215
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128231
99.1%
B 1215
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128231
99.1%
B 1215
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128231
99.1%
B 1215
 
0.9%

cat20
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129331 
B
 
115

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129331
99.9%
B 115
 
0.1%

Length

2023-09-24T16:00:45.117956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:45.301439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129331
99.9%
b 115
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 129331
99.9%
B 115
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129331
99.9%
B 115
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129331
99.9%
B 115
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129331
99.9%
B 115
 
0.1%

cat21
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129178 
B
 
268

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129178
99.8%
B 268
 
0.2%

Length

2023-09-24T16:00:45.516857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:45.696225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129178
99.8%
b 268
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 129178
99.8%
B 268
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129178
99.8%
B 268
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129178
99.8%
B 268
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129178
99.8%
B 268
 
0.2%

cat22
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129418 
B
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129418
> 99.9%
B 28
 
< 0.1%

Length

2023-09-24T16:00:45.887779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:46.066929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129418
> 99.9%
b 28
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 129418
> 99.9%
B 28
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129418
> 99.9%
B 28
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129418
> 99.9%
B 28
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129418
> 99.9%
B 28
 
< 0.1%

cat23
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
108553 
B
20893 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 108553
83.9%
B 20893
 
16.1%

Length

2023-09-24T16:00:46.252728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:46.425167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 108553
83.9%
b 20893
 
16.1%

Most occurring characters

ValueCountFrequency (%)
A 108553
83.9%
B 20893
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 108553
83.9%
B 20893
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 108553
83.9%
B 20893
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 108553
83.9%
B 20893
 
16.1%

cat24
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
125204 
B
 
4242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 125204
96.7%
B 4242
 
3.3%

Length

2023-09-24T16:00:46.627914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:46.803945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 125204
96.7%
b 4242
 
3.3%

Most occurring characters

ValueCountFrequency (%)
A 125204
96.7%
B 4242
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 125204
96.7%
B 4242
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 125204
96.7%
B 4242
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 125204
96.7%
B 4242
 
3.3%

cat25
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
116942 
B
12504 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 116942
90.3%
B 12504
 
9.7%

Length

2023-09-24T16:00:46.998292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:47.177767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 116942
90.3%
b 12504
 
9.7%

Most occurring characters

ValueCountFrequency (%)
A 116942
90.3%
B 12504
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 116942
90.3%
B 12504
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 116942
90.3%
B 12504
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 116942
90.3%
B 12504
 
9.7%

cat26
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
121758 
B
 
7688

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 121758
94.1%
B 7688
 
5.9%

Length

2023-09-24T16:00:47.378453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:47.549151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 121758
94.1%
b 7688
 
5.9%

Most occurring characters

ValueCountFrequency (%)
A 121758
94.1%
B 7688
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 121758
94.1%
B 7688
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 121758
94.1%
B 7688
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 121758
94.1%
B 7688
 
5.9%

cat27
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
115721 
B
13725 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 115721
89.4%
B 13725
 
10.6%

Length

2023-09-24T16:00:47.740908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:47.927117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 115721
89.4%
b 13725
 
10.6%

Most occurring characters

ValueCountFrequency (%)
A 115721
89.4%
B 13725
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 115721
89.4%
B 13725
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 115721
89.4%
B 13725
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 115721
89.4%
B 13725
 
10.6%

cat28
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
124493 
B
 
4953

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 124493
96.2%
B 4953
 
3.8%

Length

2023-09-24T16:00:48.159926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:48.605908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 124493
96.2%
b 4953
 
3.8%

Most occurring characters

ValueCountFrequency (%)
A 124493
96.2%
B 4953
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 124493
96.2%
B 4953
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 124493
96.2%
B 4953
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 124493
96.2%
B 4953
 
3.8%

cat29
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
126927 
B
 
2519

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 126927
98.1%
B 2519
 
1.9%

Length

2023-09-24T16:00:48.788165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:48.960646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 126927
98.1%
b 2519
 
1.9%

Most occurring characters

ValueCountFrequency (%)
A 126927
98.1%
B 2519
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 126927
98.1%
B 2519
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 126927
98.1%
B 2519
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 126927
98.1%
B 2519
 
1.9%

cat30
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
127071 
B
 
2375

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 127071
98.2%
B 2375
 
1.8%

Length

2023-09-24T16:00:49.146455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:49.316153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 127071
98.2%
b 2375
 
1.8%

Most occurring characters

ValueCountFrequency (%)
A 127071
98.2%
B 2375
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 127071
98.2%
B 2375
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 127071
98.2%
B 2375
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 127071
98.2%
B 2375
 
1.8%

cat31
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
125858 
B
 
3588

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 125858
97.2%
B 3588
 
2.8%

Length

2023-09-24T16:00:49.513850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:49.679648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 125858
97.2%
b 3588
 
2.8%

Most occurring characters

ValueCountFrequency (%)
A 125858
97.2%
B 3588
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 125858
97.2%
B 3588
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 125858
97.2%
B 3588
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 125858
97.2%
B 3588
 
2.8%

cat32
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128590 
B
 
856

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128590
99.3%
B 856
 
0.7%

Length

2023-09-24T16:00:49.874131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:50.049698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128590
99.3%
b 856
 
0.7%

Most occurring characters

ValueCountFrequency (%)
A 128590
99.3%
B 856
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128590
99.3%
B 856
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128590
99.3%
B 856
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128590
99.3%
B 856
 
0.7%

cat33
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128789 
B
 
657

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128789
99.5%
B 657
 
0.5%

Length

2023-09-24T16:00:50.236546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:50.404933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128789
99.5%
b 657
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 128789
99.5%
B 657
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128789
99.5%
B 657
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128789
99.5%
B 657
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128789
99.5%
B 657
 
0.5%

cat34
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129073 
B
 
373

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129073
99.7%
B 373
 
0.3%

Length

2023-09-24T16:00:50.604110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:50.776177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129073
99.7%
b 373
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A 129073
99.7%
B 373
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129073
99.7%
B 373
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129073
99.7%
B 373
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129073
99.7%
B 373
 
0.3%

cat35
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129314 
B
 
132

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129314
99.9%
B 132
 
0.1%

Length

2023-09-24T16:00:50.975963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:51.137618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129314
99.9%
b 132
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 129314
99.9%
B 132
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129314
99.9%
B 132
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129314
99.9%
B 132
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129314
99.9%
B 132
 
0.1%

cat36
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
107228 
B
22218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 107228
82.8%
B 22218
 
17.2%

Length

2023-09-24T16:00:51.332631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:51.497891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 107228
82.8%
b 22218
 
17.2%

Most occurring characters

ValueCountFrequency (%)
A 107228
82.8%
B 22218
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 107228
82.8%
B 22218
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 107228
82.8%
B 22218
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 107228
82.8%
B 22218
 
17.2%

cat37
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
113909 
B
15537 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 113909
88.0%
B 15537
 
12.0%

Length

2023-09-24T16:00:51.692765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:51.878070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 113909
88.0%
b 15537
 
12.0%

Most occurring characters

ValueCountFrequency (%)
A 113909
88.0%
B 15537
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 113909
88.0%
B 15537
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 113909
88.0%
B 15537
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 113909
88.0%
B 15537
 
12.0%

cat38
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
116274 
B
13172 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 116274
89.8%
B 13172
 
10.2%

Length

2023-09-24T16:00:52.079532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:52.271329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 116274
89.8%
b 13172
 
10.2%

Most occurring characters

ValueCountFrequency (%)
A 116274
89.8%
B 13172
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 116274
89.8%
B 13172
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 116274
89.8%
B 13172
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 116274
89.8%
B 13172
 
10.2%

cat39
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
125952 
B
 
3494

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 125952
97.3%
B 3494
 
2.7%

Length

2023-09-24T16:00:52.471224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:52.646343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 125952
97.3%
b 3494
 
2.7%

Most occurring characters

ValueCountFrequency (%)
A 125952
97.3%
B 3494
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 125952
97.3%
B 3494
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 125952
97.3%
B 3494
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 125952
97.3%
B 3494
 
2.7%

cat40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
123813 
B
 
5633

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 123813
95.6%
B 5633
 
4.4%

Length

2023-09-24T16:00:52.847784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:53.044855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 123813
95.6%
b 5633
 
4.4%

Most occurring characters

ValueCountFrequency (%)
A 123813
95.6%
B 5633
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 123813
95.6%
B 5633
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 123813
95.6%
B 5633
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 123813
95.6%
B 5633
 
4.4%

cat41
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
124589 
B
 
4857

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 124589
96.2%
B 4857
 
3.8%

Length

2023-09-24T16:00:53.265662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:53.444883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 124589
96.2%
b 4857
 
3.8%

Most occurring characters

ValueCountFrequency (%)
A 124589
96.2%
B 4857
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 124589
96.2%
B 4857
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 124589
96.2%
B 4857
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 124589
96.2%
B 4857
 
3.8%

cat42
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128210 
B
 
1236

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128210
99.0%
B 1236
 
1.0%

Length

2023-09-24T16:00:53.651752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:53.830148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128210
99.0%
b 1236
 
1.0%

Most occurring characters

ValueCountFrequency (%)
A 128210
99.0%
B 1236
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128210
99.0%
B 1236
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128210
99.0%
B 1236
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128210
99.0%
B 1236
 
1.0%

cat43
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
126610 
B
 
2836

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 126610
97.8%
B 2836
 
2.2%

Length

2023-09-24T16:00:54.025873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:54.218863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 126610
97.8%
b 2836
 
2.2%

Most occurring characters

ValueCountFrequency (%)
A 126610
97.8%
B 2836
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 126610
97.8%
B 2836
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 126610
97.8%
B 2836
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 126610
97.8%
B 2836
 
2.2%

cat44
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
118517 
B
 
10929

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 118517
91.6%
B 10929
 
8.4%

Length

2023-09-24T16:00:54.428523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:54.609309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 118517
91.6%
b 10929
 
8.4%

Most occurring characters

ValueCountFrequency (%)
A 118517
91.6%
B 10929
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 118517
91.6%
B 10929
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 118517
91.6%
B 10929
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 118517
91.6%
B 10929
 
8.4%

cat45
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
126453 
B
 
2993

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 126453
97.7%
B 2993
 
2.3%

Length

2023-09-24T16:00:54.808161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:54.977154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 126453
97.7%
b 2993
 
2.3%

Most occurring characters

ValueCountFrequency (%)
A 126453
97.7%
B 2993
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 126453
97.7%
B 2993
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 126453
97.7%
B 2993
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 126453
97.7%
B 2993
 
2.3%

cat46
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128853 
B
 
593

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128853
99.5%
B 593
 
0.5%

Length

2023-09-24T16:00:55.175143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:55.350919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128853
99.5%
b 593
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 128853
99.5%
B 593
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128853
99.5%
B 593
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128853
99.5%
B 593
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128853
99.5%
B 593
 
0.5%

cat47
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128943 
B
 
503

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128943
99.6%
B 503
 
0.4%

Length

2023-09-24T16:00:55.598784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:55.773542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128943
99.6%
b 503
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 128943
99.6%
B 503
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128943
99.6%
B 503
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128943
99.6%
B 503
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128943
99.6%
B 503
 
0.4%

cat48
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129262 
B
 
184

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129262
99.9%
B 184
 
0.1%

Length

2023-09-24T16:00:55.978427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:56.160492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129262
99.9%
b 184
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 129262
99.9%
B 184
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129262
99.9%
B 184
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129262
99.9%
B 184
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129262
99.9%
B 184
 
0.1%

cat49
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
123193 
B
 
6253

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 123193
95.2%
B 6253
 
4.8%

Length

2023-09-24T16:00:56.358364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:56.547220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 123193
95.2%
b 6253
 
4.8%

Most occurring characters

ValueCountFrequency (%)
A 123193
95.2%
B 6253
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 123193
95.2%
B 6253
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 123193
95.2%
B 6253
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 123193
95.2%
B 6253
 
4.8%

cat50
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
94756 
B
34690 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A 94756
73.2%
B 34690
 
26.8%

Length

2023-09-24T16:00:56.752525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:56.940517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 94756
73.2%
b 34690
 
26.8%

Most occurring characters

ValueCountFrequency (%)
A 94756
73.2%
B 34690
 
26.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 94756
73.2%
B 34690
 
26.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 94756
73.2%
B 34690
 
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 94756
73.2%
B 34690
 
26.8%

cat51
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128643 
B
 
803

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128643
99.4%
B 803
 
0.6%

Length

2023-09-24T16:00:57.154453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:57.334966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128643
99.4%
b 803
 
0.6%

Most occurring characters

ValueCountFrequency (%)
A 128643
99.4%
B 803
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128643
99.4%
B 803
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128643
99.4%
B 803
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128643
99.4%
B 803
 
0.6%

cat52
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
123487 
B
 
5959

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 123487
95.4%
B 5959
 
4.6%

Length

2023-09-24T16:00:57.534894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:57.716619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 123487
95.4%
b 5959
 
4.6%

Most occurring characters

ValueCountFrequency (%)
A 123487
95.4%
B 5959
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 123487
95.4%
B 5959
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 123487
95.4%
B 5959
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 123487
95.4%
B 5959
 
4.6%

cat53
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
118977 
B
 
10469

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 118977
91.9%
B 10469
 
8.1%

Length

2023-09-24T16:00:57.913736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:58.091253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 118977
91.9%
b 10469
 
8.1%

Most occurring characters

ValueCountFrequency (%)
A 118977
91.9%
B 10469
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 118977
91.9%
B 10469
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 118977
91.9%
B 10469
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 118977
91.9%
B 10469
 
8.1%

cat54
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
126395 
B
 
3051

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 126395
97.6%
B 3051
 
2.4%

Length

2023-09-24T16:00:58.300091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:58.746932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 126395
97.6%
b 3051
 
2.4%

Most occurring characters

ValueCountFrequency (%)
A 126395
97.6%
B 3051
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 126395
97.6%
B 3051
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 126395
97.6%
B 3051
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 126395
97.6%
B 3051
 
2.4%

cat55
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129343 
B
 
103

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129343
99.9%
B 103
 
0.1%

Length

2023-09-24T16:00:58.932358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:59.103414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129343
99.9%
b 103
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 129343
99.9%
B 103
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129343
99.9%
B 103
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129343
99.9%
B 103
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129343
99.9%
B 103
 
0.1%

cat56
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129332 
B
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129332
99.9%
B 114
 
0.1%

Length

2023-09-24T16:00:59.306557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:59.487707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129332
99.9%
b 114
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 129332
99.9%
B 114
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129332
99.9%
B 114
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129332
99.9%
B 114
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129332
99.9%
B 114
 
0.1%

cat57
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
127401 
B
 
2045

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 127401
98.4%
B 2045
 
1.6%

Length

2023-09-24T16:00:59.684133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:00:59.859287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 127401
98.4%
b 2045
 
1.6%

Most occurring characters

ValueCountFrequency (%)
A 127401
98.4%
B 2045
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 127401
98.4%
B 2045
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 127401
98.4%
B 2045
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 127401
98.4%
B 2045
 
1.6%

cat58
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129285 
B
 
161

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129285
99.9%
B 161
 
0.1%

Length

2023-09-24T16:01:00.052429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:00.232765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129285
99.9%
b 161
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 129285
99.9%
B 161
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129285
99.9%
B 161
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129285
99.9%
B 161
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129285
99.9%
B 161
 
0.1%

cat59
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129234 
B
 
212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129234
99.8%
B 212
 
0.2%

Length

2023-09-24T16:01:00.429564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:00.609923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129234
99.8%
b 212
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 129234
99.8%
B 212
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129234
99.8%
B 212
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129234
99.8%
B 212
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129234
99.8%
B 212
 
0.2%

cat60
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129176 
B
 
270

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129176
99.8%
B 270
 
0.2%

Length

2023-09-24T16:01:00.819259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:01.002777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129176
99.8%
b 270
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 129176
99.8%
B 270
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129176
99.8%
B 270
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129176
99.8%
B 270
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129176
99.8%
B 270
 
0.2%

cat61
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
128908 
B
 
538

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 128908
99.6%
B 538
 
0.4%

Length

2023-09-24T16:01:01.213229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:01.396025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128908
99.6%
b 538
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 128908
99.6%
B 538
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128908
99.6%
B 538
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128908
99.6%
B 538
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128908
99.6%
B 538
 
0.4%

cat62
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129424 
B
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129424
> 99.9%
B 22
 
< 0.1%

Length

2023-09-24T16:01:01.601980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:01.787505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129424
> 99.9%
b 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 129424
> 99.9%
B 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129424
> 99.9%
B 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129424
> 99.9%
B 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129424
> 99.9%
B 22
 
< 0.1%

cat63
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129398 
B
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129398
> 99.9%
B 48
 
< 0.1%

Length

2023-09-24T16:01:01.977791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:02.147948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129398
> 99.9%
b 48
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 129398
> 99.9%
B 48
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129398
> 99.9%
B 48
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129398
> 99.9%
B 48
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129398
> 99.9%
B 48
 
< 0.1%

cat64
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129422 
B
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129422
> 99.9%
B 24
 
< 0.1%

Length

2023-09-24T16:01:02.341385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:02.516180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129422
> 99.9%
b 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 129422
> 99.9%
B 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129422
> 99.9%
B 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129422
> 99.9%
B 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129422
> 99.9%
B 24
 
< 0.1%

cat65
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
127872 
B
 
1574

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 127872
98.8%
B 1574
 
1.2%

Length

2023-09-24T16:01:02.709608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:02.903834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 127872
98.8%
b 1574
 
1.2%

Most occurring characters

ValueCountFrequency (%)
A 127872
98.8%
B 1574
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 127872
98.8%
B 1574
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 127872
98.8%
B 1574
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 127872
98.8%
B 1574
 
1.2%

cat66
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
123658 
B
 
5788

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 123658
95.5%
B 5788
 
4.5%

Length

2023-09-24T16:01:03.107463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:03.299190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 123658
95.5%
b 5788
 
4.5%

Most occurring characters

ValueCountFrequency (%)
A 123658
95.5%
B 5788
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 123658
95.5%
B 5788
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 123658
95.5%
B 5788
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 123658
95.5%
B 5788
 
4.5%

cat67
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129001 
B
 
445

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129001
99.7%
B 445
 
0.3%

Length

2023-09-24T16:01:03.519210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:03.708445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129001
99.7%
b 445
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A 129001
99.7%
B 445
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129001
99.7%
B 445
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129001
99.7%
B 445
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129001
99.7%
B 445
 
0.3%

cat68
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129334 
B
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129334
99.9%
B 112
 
0.1%

Length

2023-09-24T16:01:03.916655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:04.092547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129334
99.9%
b 112
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 129334
99.9%
B 112
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129334
99.9%
B 112
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129334
99.9%
B 112
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129334
99.9%
B 112
 
0.1%

cat69
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129243 
B
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129243
99.8%
B 203
 
0.2%

Length

2023-09-24T16:01:04.299385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:04.484636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129243
99.8%
b 203
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 129243
99.8%
B 203
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129243
99.8%
B 203
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129243
99.8%
B 203
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129243
99.8%
B 203
 
0.2%

cat70
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
129425 
B
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 129425
> 99.9%
B 21
 
< 0.1%

Length

2023-09-24T16:01:04.681589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:04.854528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 129425
> 99.9%
b 21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 129425
> 99.9%
B 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 129425
> 99.9%
B 21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 129425
> 99.9%
B 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 129425
> 99.9%
B 21
 
< 0.1%

cat71
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
122894 
B
 
6552

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 122894
94.9%
B 6552
 
5.1%

Length

2023-09-24T16:01:05.053610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:05.221385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 122894
94.9%
b 6552
 
5.1%

Most occurring characters

ValueCountFrequency (%)
A 122894
94.9%
B 6552
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 122894
94.9%
B 6552
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 122894
94.9%
B 6552
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 122894
94.9%
B 6552
 
5.1%

cat72
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
82003 
B
47443 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 82003
63.3%
B 47443
36.7%

Length

2023-09-24T16:01:05.425388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:05.604118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 82003
63.3%
b 47443
36.7%

Most occurring characters

ValueCountFrequency (%)
A 82003
63.3%
B 47443
36.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 82003
63.3%
B 47443
36.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 82003
63.3%
B 47443
36.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 82003
63.3%
B 47443
36.7%

cat73
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
105754 
B
23668 
C
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 105754
81.7%
B 23668
 
18.3%
C 24
 
< 0.1%

Length

2023-09-24T16:01:05.810293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:05.987274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 105754
81.7%
b 23668
 
18.3%
c 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 105754
81.7%
B 23668
 
18.3%
C 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 105754
81.7%
B 23668
 
18.3%
C 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 105754
81.7%
B 23668
 
18.3%
C 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 105754
81.7%
B 23668
 
18.3%
C 24
 
< 0.1%

cat74
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
126865 
B
 
2557
C
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 126865
98.0%
B 2557
 
2.0%
C 24
 
< 0.1%

Length

2023-09-24T16:01:06.187329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:06.377880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 126865
98.0%
b 2557
 
2.0%
c 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 126865
98.0%
B 2557
 
2.0%
C 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 126865
98.0%
B 2557
 
2.0%
C 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 126865
98.0%
B 2557
 
2.0%
C 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 126865
98.0%
B 2557
 
2.0%
C 24
 
< 0.1%

cat75
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
106053 
B
23391 
C
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 106053
81.9%
B 23391
 
18.1%
C 2
 
< 0.1%

Length

2023-09-24T16:01:06.574739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:06.753892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 106053
81.9%
b 23391
 
18.1%
c 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 106053
81.9%
B 23391
 
18.1%
C 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 106053
81.9%
B 23391
 
18.1%
C 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 106053
81.9%
B 23391
 
18.1%
C 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 106053
81.9%
B 23391
 
18.1%
C 2
 
< 0.1%

cat76
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
124625 
B
 
4253
C
 
568

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 124625
96.3%
B 4253
 
3.3%
C 568
 
0.4%

Length

2023-09-24T16:01:06.953257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:07.137248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 124625
96.3%
b 4253
 
3.3%
c 568
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 124625
96.3%
B 4253
 
3.3%
C 568
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 124625
96.3%
B 4253
 
3.3%
C 568
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 124625
96.3%
B 4253
 
3.3%
C 568
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 124625
96.3%
B 4253
 
3.3%
C 568
 
0.4%

cat77
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
D
128863 
C
 
277
B
 
272
A
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 128863
99.5%
C 277
 
0.2%
B 272
 
0.2%
A 34
 
< 0.1%

Length

2023-09-24T16:01:07.336425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:07.526201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 128863
99.5%
c 277
 
0.2%
b 272
 
0.2%
a 34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 128863
99.5%
C 277
 
0.2%
B 272
 
0.2%
A 34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 128863
99.5%
C 277
 
0.2%
B 272
 
0.2%
A 34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 128863
99.5%
C 277
 
0.2%
B 272
 
0.2%
A 34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 128863
99.5%
C 277
 
0.2%
B 272
 
0.2%
A 34
 
< 0.1%

cat78
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
B
128193 
A
 
572
C
 
455
D
 
226

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 128193
99.0%
A 572
 
0.4%
C 455
 
0.4%
D 226
 
0.2%

Length

2023-09-24T16:01:07.727231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:07.909076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 128193
99.0%
a 572
 
0.4%
c 455
 
0.4%
d 226
 
0.2%

Most occurring characters

ValueCountFrequency (%)
B 128193
99.0%
A 572
 
0.4%
C 455
 
0.4%
D 226
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 128193
99.0%
A 572
 
0.4%
C 455
 
0.4%
D 226
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 128193
99.0%
A 572
 
0.4%
C 455
 
0.4%
D 226
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 128193
99.0%
A 572
 
0.4%
C 455
 
0.4%
D 226
 
0.2%

cat79
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
B
105231 
D
18207 
A
 
4815
C
 
1193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
B 105231
81.3%
D 18207
 
14.1%
A 4815
 
3.7%
C 1193
 
0.9%

Length

2023-09-24T16:01:08.114563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:08.307545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 105231
81.3%
d 18207
 
14.1%
a 4815
 
3.7%
c 1193
 
0.9%

Most occurring characters

ValueCountFrequency (%)
B 105231
81.3%
D 18207
 
14.1%
A 4815
 
3.7%
C 1193
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 105231
81.3%
D 18207
 
14.1%
A 4815
 
3.7%
C 1193
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 105231
81.3%
D 18207
 
14.1%
A 4815
 
3.7%
C 1193
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 105231
81.3%
D 18207
 
14.1%
A 4815
 
3.7%
C 1193
 
0.9%

cat80
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
D
94727 
B
31659 
C
 
2495
A
 
565

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowB
4th rowB
5th rowD

Common Values

ValueCountFrequency (%)
D 94727
73.2%
B 31659
 
24.5%
C 2495
 
1.9%
A 565
 
0.4%

Length

2023-09-24T16:01:08.516449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:08.990767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 94727
73.2%
b 31659
 
24.5%
c 2495
 
1.9%
a 565
 
0.4%

Most occurring characters

ValueCountFrequency (%)
D 94727
73.2%
B 31659
 
24.5%
C 2495
 
1.9%
A 565
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 94727
73.2%
B 31659
 
24.5%
C 2495
 
1.9%
A 565
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 94727
73.2%
B 31659
 
24.5%
C 2495
 
1.9%
A 565
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 94727
73.2%
B 31659
 
24.5%
C 2495
 
1.9%
A 565
 
0.4%

cat81
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
D
106133 
B
16310 
C
 
6431
A
 
572

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowB
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 106133
82.0%
B 16310
 
12.6%
C 6431
 
5.0%
A 572
 
0.4%

Length

2023-09-24T16:01:09.203120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:09.395040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 106133
82.0%
b 16310
 
12.6%
c 6431
 
5.0%
a 572
 
0.4%

Most occurring characters

ValueCountFrequency (%)
D 106133
82.0%
B 16310
 
12.6%
C 6431
 
5.0%
A 572
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 106133
82.0%
B 16310
 
12.6%
C 6431
 
5.0%
A 572
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 106133
82.0%
B 16310
 
12.6%
C 6431
 
5.0%
A 572
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 106133
82.0%
B 16310
 
12.6%
C 6431
 
5.0%
A 572
 
0.4%

cat82
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
B
101039 
A
13427 
D
13202 
C
 
1778

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 101039
78.1%
A 13427
 
10.4%
D 13202
 
10.2%
C 1778
 
1.4%

Length

2023-09-24T16:01:09.603534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:09.784148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 101039
78.1%
a 13427
 
10.4%
d 13202
 
10.2%
c 1778
 
1.4%

Most occurring characters

ValueCountFrequency (%)
B 101039
78.1%
A 13427
 
10.4%
D 13202
 
10.2%
C 1778
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 101039
78.1%
A 13427
 
10.4%
D 13202
 
10.2%
C 1778
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 101039
78.1%
A 13427
 
10.4%
D 13202
 
10.2%
C 1778
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 101039
78.1%
A 13427
 
10.4%
D 13202
 
10.2%
C 1778
 
1.4%

cat83
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
B
97047 
A
17905 
D
10935 
C
 
3559

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowD
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 97047
75.0%
A 17905
 
13.8%
D 10935
 
8.4%
C 3559
 
2.7%

Length

2023-09-24T16:01:09.981204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:10.168700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 97047
75.0%
a 17905
 
13.8%
d 10935
 
8.4%
c 3559
 
2.7%

Most occurring characters

ValueCountFrequency (%)
B 97047
75.0%
A 17905
 
13.8%
D 10935
 
8.4%
C 3559
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 97047
75.0%
A 17905
 
13.8%
D 10935
 
8.4%
C 3559
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 97047
75.0%
A 17905
 
13.8%
D 10935
 
8.4%
C 3559
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 97047
75.0%
A 17905
 
13.8%
D 10935
 
8.4%
C 3559
 
2.7%

cat84
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
C
106433 
A
20410 
D
 
2315
B
 
288

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowC

Common Values

ValueCountFrequency (%)
C 106433
82.2%
A 20410
 
15.8%
D 2315
 
1.8%
B 288
 
0.2%

Length

2023-09-24T16:01:10.373814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:10.567049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 106433
82.2%
a 20410
 
15.8%
d 2315
 
1.8%
b 288
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 106433
82.2%
A 20410
 
15.8%
D 2315
 
1.8%
B 288
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 106433
82.2%
A 20410
 
15.8%
D 2315
 
1.8%
B 288
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 106433
82.2%
A 20410
 
15.8%
D 2315
 
1.8%
B 288
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 106433
82.2%
A 20410
 
15.8%
D 2315
 
1.8%
B 288
 
0.2%

cat85
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
B
127826 
C
 
714
A
 
572
D
 
334

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 127826
98.7%
C 714
 
0.6%
A 572
 
0.4%
D 334
 
0.3%

Length

2023-09-24T16:01:10.777950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:10.967331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 127826
98.7%
c 714
 
0.6%
a 572
 
0.4%
d 334
 
0.3%

Most occurring characters

ValueCountFrequency (%)
B 127826
98.7%
C 714
 
0.6%
A 572
 
0.4%
D 334
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 127826
98.7%
C 714
 
0.6%
A 572
 
0.4%
D 334
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 127826
98.7%
C 714
 
0.6%
A 572
 
0.4%
D 334
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 127826
98.7%
C 714
 
0.6%
A 572
 
0.4%
D 334
 
0.3%

cat86
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
B
70742 
D
50574 
C
 
6972
A
 
1158

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowB
3rd rowB
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
B 70742
54.6%
D 50574
39.1%
C 6972
 
5.4%
A 1158
 
0.9%

Length

2023-09-24T16:01:11.169077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:11.359164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 70742
54.6%
d 50574
39.1%
c 6972
 
5.4%
a 1158
 
0.9%

Most occurring characters

ValueCountFrequency (%)
B 70742
54.6%
D 50574
39.1%
C 6972
 
5.4%
A 1158
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 70742
54.6%
D 50574
39.1%
C 6972
 
5.4%
A 1158
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 70742
54.6%
D 50574
39.1%
C 6972
 
5.4%
A 1158
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 70742
54.6%
D 50574
39.1%
C 6972
 
5.4%
A 1158
 
0.9%

cat87
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
B
114785 
D
 
8058
C
 
6031
A
 
572

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
B 114785
88.7%
D 8058
 
6.2%
C 6031
 
4.7%
A 572
 
0.4%

Length

2023-09-24T16:01:11.573714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:11.763677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 114785
88.7%
d 8058
 
6.2%
c 6031
 
4.7%
a 572
 
0.4%

Most occurring characters

ValueCountFrequency (%)
B 114785
88.7%
D 8058
 
6.2%
C 6031
 
4.7%
A 572
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 114785
88.7%
D 8058
 
6.2%
C 6031
 
4.7%
A 572
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 114785
88.7%
D 8058
 
6.2%
C 6031
 
4.7%
A 572
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 114785
88.7%
D 8058
 
6.2%
C 6031
 
4.7%
A 572
 
0.4%

cat88
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
115936 
D
13424 
E
 
74
B
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 115936
89.6%
D 13424
 
10.4%
E 74
 
0.1%
B 12
 
< 0.1%

Length

2023-09-24T16:01:11.971296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:12.165136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 115936
89.6%
d 13424
 
10.4%
e 74
 
0.1%
b 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 115936
89.6%
D 13424
 
10.4%
E 74
 
0.1%
B 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 115936
89.6%
D 13424
 
10.4%
E 74
 
0.1%
B 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 115936
89.6%
D 13424
 
10.4%
E 74
 
0.1%
B 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 115936
89.6%
D 13424
 
10.4%
E 74
 
0.1%
B 12
 
< 0.1%

cat89
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
126349 
B
 
2922
C
 
147
D
 
20
F
 
2
Other values (3)
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 126349
97.6%
B 2922
 
2.3%
C 147
 
0.1%
D 20
 
< 0.1%
F 2
 
< 0.1%
H 2
 
< 0.1%
E 2
 
< 0.1%
G 2
 
< 0.1%

Length

2023-09-24T16:01:12.377091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:12.586202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 126349
97.6%
b 2922
 
2.3%
c 147
 
0.1%
d 20
 
< 0.1%
f 2
 
< 0.1%
h 2
 
< 0.1%
e 2
 
< 0.1%
g 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 126349
97.6%
B 2922
 
2.3%
C 147
 
0.1%
D 20
 
< 0.1%
F 2
 
< 0.1%
H 2
 
< 0.1%
E 2
 
< 0.1%
G 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 126349
97.6%
B 2922
 
2.3%
C 147
 
0.1%
D 20
 
< 0.1%
F 2
 
< 0.1%
H 2
 
< 0.1%
E 2
 
< 0.1%
G 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 126349
97.6%
B 2922
 
2.3%
C 147
 
0.1%
D 20
 
< 0.1%
F 2
 
< 0.1%
H 2
 
< 0.1%
E 2
 
< 0.1%
G 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 126349
97.6%
B 2922
 
2.3%
C 147
 
0.1%
D 20
 
< 0.1%
F 2
 
< 0.1%
H 2
 
< 0.1%
E 2
 
< 0.1%
G 2
 
< 0.1%

cat90
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
122432 
B
 
6477
C
 
483
D
 
46
E
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 122432
94.6%
B 6477
 
5.0%
C 483
 
0.4%
D 46
 
< 0.1%
E 5
 
< 0.1%
F 3
 
< 0.1%

Length

2023-09-24T16:01:12.795455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:12.986203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 122432
94.6%
b 6477
 
5.0%
c 483
 
0.4%
d 46
 
< 0.1%
e 5
 
< 0.1%
f 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 122432
94.6%
B 6477
 
5.0%
C 483
 
0.4%
D 46
 
< 0.1%
E 5
 
< 0.1%
F 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 122432
94.6%
B 6477
 
5.0%
C 483
 
0.4%
D 46
 
< 0.1%
E 5
 
< 0.1%
F 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 122432
94.6%
B 6477
 
5.0%
C 483
 
0.4%
D 46
 
< 0.1%
E 5
 
< 0.1%
F 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 122432
94.6%
B 6477
 
5.0%
C 483
 
0.4%
D 46
 
< 0.1%
E 5
 
< 0.1%
F 3
 
< 0.1%

cat91
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
76084 
B
29372 
G
18421 
C
 
4499
D
 
811
Other values (3)
 
259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowG
5th rowA

Common Values

ValueCountFrequency (%)
A 76084
58.8%
B 29372
 
22.7%
G 18421
 
14.2%
C 4499
 
3.5%
D 811
 
0.6%
E 156
 
0.1%
F 79
 
0.1%
H 24
 
< 0.1%

Length

2023-09-24T16:01:13.186130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:13.400361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 76084
58.8%
b 29372
 
22.7%
g 18421
 
14.2%
c 4499
 
3.5%
d 811
 
0.6%
e 156
 
0.1%
f 79
 
0.1%
h 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 76084
58.8%
B 29372
 
22.7%
G 18421
 
14.2%
C 4499
 
3.5%
D 811
 
0.6%
E 156
 
0.1%
F 79
 
0.1%
H 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 76084
58.8%
B 29372
 
22.7%
G 18421
 
14.2%
C 4499
 
3.5%
D 811
 
0.6%
E 156
 
0.1%
F 79
 
0.1%
H 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 76084
58.8%
B 29372
 
22.7%
G 18421
 
14.2%
C 4499
 
3.5%
D 811
 
0.6%
E 156
 
0.1%
F 79
 
0.1%
H 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 76084
58.8%
B 29372
 
22.7%
G 18421
 
14.2%
C 4499
 
3.5%
D 811
 
0.6%
E 156
 
0.1%
F 79
 
0.1%
H 24
 
< 0.1%

cat92
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
85619 
H
43282 
B
 
467
C
 
49
I
 
24
Other values (3)
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowH
5th rowA

Common Values

ValueCountFrequency (%)
A 85619
66.1%
H 43282
33.4%
B 467
 
0.4%
C 49
 
< 0.1%
I 24
 
< 0.1%
D 2
 
< 0.1%
E 2
 
< 0.1%
G 1
 
< 0.1%

Length

2023-09-24T16:01:13.610448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:13.837283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 85619
66.1%
h 43282
33.4%
b 467
 
0.4%
c 49
 
< 0.1%
i 24
 
< 0.1%
d 2
 
< 0.1%
e 2
 
< 0.1%
g 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 85619
66.1%
H 43282
33.4%
B 467
 
0.4%
C 49
 
< 0.1%
I 24
 
< 0.1%
D 2
 
< 0.1%
E 2
 
< 0.1%
G 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 85619
66.1%
H 43282
33.4%
B 467
 
0.4%
C 49
 
< 0.1%
I 24
 
< 0.1%
D 2
 
< 0.1%
E 2
 
< 0.1%
G 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 85619
66.1%
H 43282
33.4%
B 467
 
0.4%
C 49
 
< 0.1%
I 24
 
< 0.1%
D 2
 
< 0.1%
E 2
 
< 0.1%
G 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 85619
66.1%
H 43282
33.4%
B 467
 
0.4%
C 49
 
< 0.1%
I 24
 
< 0.1%
D 2
 
< 0.1%
E 2
 
< 0.1%
G 1
 
< 0.1%

cat93
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
D
103191 
C
24654 
B
 
803
E
 
482
A
 
316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 103191
79.7%
C 24654
 
19.0%
B 803
 
0.6%
E 482
 
0.4%
A 316
 
0.2%

Length

2023-09-24T16:01:14.057037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:14.246848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 103191
79.7%
c 24654
 
19.0%
b 803
 
0.6%
e 482
 
0.4%
a 316
 
0.2%

Most occurring characters

ValueCountFrequency (%)
D 103191
79.7%
C 24654
 
19.0%
B 803
 
0.6%
E 482
 
0.4%
A 316
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 103191
79.7%
C 24654
 
19.0%
B 803
 
0.6%
E 482
 
0.4%
A 316
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 103191
79.7%
C 24654
 
19.0%
B 803
 
0.6%
E 482
 
0.4%
A 316
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 103191
79.7%
C 24654
 
19.0%
B 803
 
0.6%
E 482
 
0.4%
A 316
 
0.2%

cat94
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
D
83478 
B
35554 
C
9491 
A
 
500
F
 
351
Other values (2)
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowD
3rd rowD
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
D 83478
64.5%
B 35554
27.5%
C 9491
 
7.3%
A 500
 
0.4%
F 351
 
0.3%
E 59
 
< 0.1%
G 13
 
< 0.1%

Length

2023-09-24T16:01:14.456592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:14.658418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 83478
64.5%
b 35554
27.5%
c 9491
 
7.3%
a 500
 
0.4%
f 351
 
0.3%
e 59
 
< 0.1%
g 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 83478
64.5%
B 35554
27.5%
C 9491
 
7.3%
A 500
 
0.4%
F 351
 
0.3%
E 59
 
< 0.1%
G 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 83478
64.5%
B 35554
27.5%
C 9491
 
7.3%
A 500
 
0.4%
F 351
 
0.3%
E 59
 
< 0.1%
G 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 83478
64.5%
B 35554
27.5%
C 9491
 
7.3%
A 500
 
0.4%
F 351
 
0.3%
E 59
 
< 0.1%
G 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 83478
64.5%
B 35554
27.5%
C 9491
 
7.3%
A 500
 
0.4%
F 351
 
0.3%
E 59
 
< 0.1%
G 13
 
< 0.1%

cat95
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
C
60686 
D
54326 
E
11842 
A
 
2528
B
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowD
3rd rowC
4th rowC
5th rowD

Common Values

ValueCountFrequency (%)
C 60686
46.9%
D 54326
42.0%
E 11842
 
9.1%
A 2528
 
2.0%
B 64
 
< 0.1%

Length

2023-09-24T16:01:14.885866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:15.076664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 60686
46.9%
d 54326
42.0%
e 11842
 
9.1%
a 2528
 
2.0%
b 64
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 60686
46.9%
D 54326
42.0%
E 11842
 
9.1%
A 2528
 
2.0%
B 64
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 60686
46.9%
D 54326
42.0%
E 11842
 
9.1%
A 2528
 
2.0%
B 64
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 60686
46.9%
D 54326
42.0%
E 11842
 
9.1%
A 2528
 
2.0%
B 64
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 60686
46.9%
D 54326
42.0%
E 11842
 
9.1%
A 2528
 
2.0%
B 64
 
< 0.1%

cat96
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
E
119773 
D
 
5591
B
 
1961
G
 
1848
F
 
235
Other values (4)
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 119773
92.5%
D 5591
 
4.3%
B 1961
 
1.5%
G 1848
 
1.4%
F 235
 
0.2%
A 19
 
< 0.1%
C 10
 
< 0.1%
I 8
 
< 0.1%
H 1
 
< 0.1%

Length

2023-09-24T16:01:15.283514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:15.503423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 119773
92.5%
d 5591
 
4.3%
b 1961
 
1.5%
g 1848
 
1.4%
f 235
 
0.2%
a 19
 
< 0.1%
c 10
 
< 0.1%
i 8
 
< 0.1%
h 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 119773
92.5%
D 5591
 
4.3%
B 1961
 
1.5%
G 1848
 
1.4%
F 235
 
0.2%
A 19
 
< 0.1%
C 10
 
< 0.1%
I 8
 
< 0.1%
H 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 119773
92.5%
D 5591
 
4.3%
B 1961
 
1.5%
G 1848
 
1.4%
F 235
 
0.2%
A 19
 
< 0.1%
C 10
 
< 0.1%
I 8
 
< 0.1%
H 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 119773
92.5%
D 5591
 
4.3%
B 1961
 
1.5%
G 1848
 
1.4%
F 235
 
0.2%
A 19
 
< 0.1%
C 10
 
< 0.1%
I 8
 
< 0.1%
H 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 119773
92.5%
D 5591
 
4.3%
B 1961
 
1.5%
G 1848
 
1.4%
F 235
 
0.2%
A 19
 
< 0.1%
C 10
 
< 0.1%
I 8
 
< 0.1%
H 1
 
< 0.1%

cat97
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
C
53383 
E
32648 
A
29359 
G
11288 
D
 
2606
Other values (2)
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowA
3rd rowE
4th rowE
5th rowA

Common Values

ValueCountFrequency (%)
C 53383
41.2%
E 32648
25.2%
A 29359
22.7%
G 11288
 
8.7%
D 2606
 
2.0%
F 138
 
0.1%
B 24
 
< 0.1%

Length

2023-09-24T16:01:15.722276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:15.925375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 53383
41.2%
e 32648
25.2%
a 29359
22.7%
g 11288
 
8.7%
d 2606
 
2.0%
f 138
 
0.1%
b 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 53383
41.2%
E 32648
25.2%
A 29359
22.7%
G 11288
 
8.7%
D 2606
 
2.0%
F 138
 
0.1%
B 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 53383
41.2%
E 32648
25.2%
A 29359
22.7%
G 11288
 
8.7%
D 2606
 
2.0%
F 138
 
0.1%
B 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 53383
41.2%
E 32648
25.2%
A 29359
22.7%
G 11288
 
8.7%
D 2606
 
2.0%
F 138
 
0.1%
B 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 53383
41.2%
E 32648
25.2%
A 29359
22.7%
G 11288
 
8.7%
D 2606
 
2.0%
F 138
 
0.1%
B 24
 
< 0.1%

cat98
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
71927 
D
35238 
C
14937 
E
 
6946
B
 
398

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowA
3rd rowA
4th rowD
5th rowA

Common Values

ValueCountFrequency (%)
A 71927
55.6%
D 35238
27.2%
C 14937
 
11.5%
E 6946
 
5.4%
B 398
 
0.3%

Length

2023-09-24T16:01:16.129303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:16.321355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 71927
55.6%
d 35238
27.2%
c 14937
 
11.5%
e 6946
 
5.4%
b 398
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A 71927
55.6%
D 35238
27.2%
C 14937
 
11.5%
E 6946
 
5.4%
B 398
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 71927
55.6%
D 35238
27.2%
C 14937
 
11.5%
E 6946
 
5.4%
B 398
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 71927
55.6%
D 35238
27.2%
C 14937
 
11.5%
E 6946
 
5.4%
B 398
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 71927
55.6%
D 35238
27.2%
C 14937
 
11.5%
E 6946
 
5.4%
B 398
 
0.3%

cat99
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
P
54280 
T
50584 
R
6972 
D
5902 
S
 
4809
Other values (12)
6899 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowT
2nd rowP
3rd rowD
4th rowT
5th rowP

Common Values

ValueCountFrequency (%)
P 54280
41.9%
T 50584
39.1%
R 6972
 
5.4%
D 5902
 
4.6%
S 4809
 
3.7%
N 1932
 
1.5%
K 1896
 
1.5%
F 1782
 
1.4%
E 746
 
0.6%
C 237
 
0.2%
Other values (7) 306
 
0.2%

Length

2023-09-24T16:01:16.533433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p 54280
41.9%
t 50584
39.1%
r 6972
 
5.4%
d 5902
 
4.6%
s 4809
 
3.7%
n 1932
 
1.5%
k 1896
 
1.5%
f 1782
 
1.4%
e 746
 
0.6%
c 237
 
0.2%
Other values (7) 306
 
0.2%

Most occurring characters

ValueCountFrequency (%)
P 54280
41.9%
T 50584
39.1%
R 6972
 
5.4%
D 5902
 
4.6%
S 4809
 
3.7%
N 1932
 
1.5%
K 1896
 
1.5%
F 1782
 
1.4%
E 746
 
0.6%
C 237
 
0.2%
Other values (7) 306
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 54280
41.9%
T 50584
39.1%
R 6972
 
5.4%
D 5902
 
4.6%
S 4809
 
3.7%
N 1932
 
1.5%
K 1896
 
1.5%
F 1782
 
1.4%
E 746
 
0.6%
C 237
 
0.2%
Other values (7) 306
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 54280
41.9%
T 50584
39.1%
R 6972
 
5.4%
D 5902
 
4.6%
S 4809
 
3.7%
N 1932
 
1.5%
K 1896
 
1.5%
F 1782
 
1.4%
E 746
 
0.6%
C 237
 
0.2%
Other values (7) 306
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 54280
41.9%
T 50584
39.1%
R 6972
 
5.4%
D 5902
 
4.6%
S 4809
 
3.7%
N 1932
 
1.5%
K 1896
 
1.5%
F 1782
 
1.4%
E 746
 
0.6%
C 237
 
0.2%
Other values (7) 306
 
0.2%

cat100
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
F
29757 
I
27543 
L
13685 
K
9307 
G
8907 
Other values (10)
40247 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowH
2nd rowB
3rd rowG
4th rowG
5th rowA

Common Values

ValueCountFrequency (%)
F 29757
23.0%
I 27543
21.3%
L 13685
10.6%
K 9307
 
7.2%
G 8907
 
6.9%
J 8221
 
6.4%
H 7244
 
5.6%
A 6493
 
5.0%
N 5256
 
4.1%
B 4490
 
3.5%
Other values (5) 8543
 
6.6%

Length

2023-09-24T16:01:16.739416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f 29757
23.0%
i 27543
21.3%
l 13685
10.6%
k 9307
 
7.2%
g 8907
 
6.9%
j 8221
 
6.4%
h 7244
 
5.6%
a 6493
 
5.0%
n 5256
 
4.1%
b 4490
 
3.5%
Other values (5) 8543
 
6.6%

Most occurring characters

ValueCountFrequency (%)
F 29757
23.0%
I 27543
21.3%
L 13685
10.6%
K 9307
 
7.2%
G 8907
 
6.9%
J 8221
 
6.4%
H 7244
 
5.6%
A 6493
 
5.0%
N 5256
 
4.1%
B 4490
 
3.5%
Other values (5) 8543
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 29757
23.0%
I 27543
21.3%
L 13685
10.6%
K 9307
 
7.2%
G 8907
 
6.9%
J 8221
 
6.4%
H 7244
 
5.6%
A 6493
 
5.0%
N 5256
 
4.1%
B 4490
 
3.5%
Other values (5) 8543
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 29757
23.0%
I 27543
21.3%
L 13685
10.6%
K 9307
 
7.2%
G 8907
 
6.9%
J 8221
 
6.4%
H 7244
 
5.6%
A 6493
 
5.0%
N 5256
 
4.1%
B 4490
 
3.5%
Other values (5) 8543
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 29757
23.0%
I 27543
21.3%
L 13685
10.6%
K 9307
 
7.2%
G 8907
 
6.9%
J 8221
 
6.4%
H 7244
 
5.6%
A 6493
 
5.0%
N 5256
 
4.1%
B 4490
 
3.5%
Other values (5) 8543
 
6.6%

cat101
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
73448 
D
12019 
C
11561 
G
7488 
F
 
7114
Other values (12)
17816 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowG
2nd rowD
3rd rowQ
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 73448
56.7%
D 12019
 
9.3%
C 11561
 
8.9%
G 7488
 
5.8%
F 7114
 
5.5%
J 4958
 
3.8%
I 4510
 
3.5%
M 2487
 
1.9%
L 2122
 
1.6%
Q 1837
 
1.4%
Other values (7) 1902
 
1.5%

Length

2023-09-24T16:01:16.971714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 73448
56.7%
d 12019
 
9.3%
c 11561
 
8.9%
g 7488
 
5.8%
f 7114
 
5.5%
j 4958
 
3.8%
i 4510
 
3.5%
m 2487
 
1.9%
l 2122
 
1.6%
q 1837
 
1.4%
Other values (7) 1902
 
1.5%

Most occurring characters

ValueCountFrequency (%)
A 73448
56.7%
D 12019
 
9.3%
C 11561
 
8.9%
G 7488
 
5.8%
F 7114
 
5.5%
J 4958
 
3.8%
I 4510
 
3.5%
M 2487
 
1.9%
L 2122
 
1.6%
Q 1837
 
1.4%
Other values (7) 1902
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 73448
56.7%
D 12019
 
9.3%
C 11561
 
8.9%
G 7488
 
5.8%
F 7114
 
5.5%
J 4958
 
3.8%
I 4510
 
3.5%
M 2487
 
1.9%
L 2122
 
1.6%
Q 1837
 
1.4%
Other values (7) 1902
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 73448
56.7%
D 12019
 
9.3%
C 11561
 
8.9%
G 7488
 
5.8%
F 7114
 
5.5%
J 4958
 
3.8%
I 4510
 
3.5%
M 2487
 
1.9%
L 2122
 
1.6%
Q 1837
 
1.4%
Other values (7) 1902
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 73448
56.7%
D 12019
 
9.3%
C 11561
 
8.9%
G 7488
 
5.8%
F 7114
 
5.5%
J 4958
 
3.8%
I 4510
 
3.5%
M 2487
 
1.9%
L 2122
 
1.6%
Q 1837
 
1.4%
Other values (7) 1902
 
1.5%

cat102
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
121786 
B
 
3622
C
 
3392
E
 
322
D
 
301
Other values (2)
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 121786
94.1%
B 3622
 
2.8%
C 3392
 
2.6%
E 322
 
0.2%
D 301
 
0.2%
G 16
 
< 0.1%
F 7
 
< 0.1%

Length

2023-09-24T16:01:17.182755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T16:01:17.385767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 121786
94.1%
b 3622
 
2.8%
c 3392
 
2.6%
e 322
 
0.2%
d 301
 
0.2%
g 16
 
< 0.1%
f 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 121786
94.1%
B 3622
 
2.8%
C 3392
 
2.6%
E 322
 
0.2%
D 301
 
0.2%
G 16
 
< 0.1%
F 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 121786
94.1%
B 3622
 
2.8%
C 3392
 
2.6%
E 322
 
0.2%
D 301
 
0.2%
G 16
 
< 0.1%
F 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 121786
94.1%
B 3622
 
2.8%
C 3392
 
2.6%
E 322
 
0.2%
D 301
 
0.2%
G 16
 
< 0.1%
F 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 121786
94.1%
B 3622
 
2.8%
C 3392
 
2.6%
E 322
 
0.2%
D 301
 
0.2%
G 16
 
< 0.1%
F 7
 
< 0.1%

cat103
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
84753 
B
22881 
C
11660 
D
 
5356
E
 
3166
Other values (9)
 
1630

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowA
3rd rowD
4th rowD
5th rowA

Common Values

ValueCountFrequency (%)
A 84753
65.5%
B 22881
 
17.7%
C 11660
 
9.0%
D 5356
 
4.1%
E 3166
 
2.4%
F 999
 
0.8%
G 369
 
0.3%
H 146
 
0.1%
I 60
 
< 0.1%
J 33
 
< 0.1%
Other values (4) 23
 
< 0.1%

Length

2023-09-24T16:01:17.596465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 84753
65.5%
b 22881
 
17.7%
c 11660
 
9.0%
d 5356
 
4.1%
e 3166
 
2.4%
f 999
 
0.8%
g 369
 
0.3%
h 146
 
0.1%
i 60
 
< 0.1%
j 33
 
< 0.1%
Other values (4) 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 84753
65.5%
B 22881
 
17.7%
C 11660
 
9.0%
D 5356
 
4.1%
E 3166
 
2.4%
F 999
 
0.8%
G 369
 
0.3%
H 146
 
0.1%
I 60
 
< 0.1%
J 33
 
< 0.1%
Other values (4) 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 84753
65.5%
B 22881
 
17.7%
C 11660
 
9.0%
D 5356
 
4.1%
E 3166
 
2.4%
F 999
 
0.8%
G 369
 
0.3%
H 146
 
0.1%
I 60
 
< 0.1%
J 33
 
< 0.1%
Other values (4) 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 84753
65.5%
B 22881
 
17.7%
C 11660
 
9.0%
D 5356
 
4.1%
E 3166
 
2.4%
F 999
 
0.8%
G 369
 
0.3%
H 146
 
0.1%
I 60
 
< 0.1%
J 33
 
< 0.1%
Other values (4) 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 84753
65.5%
B 22881
 
17.7%
C 11660
 
9.0%
D 5356
 
4.1%
E 3166
 
2.4%
F 999
 
0.8%
G 369
 
0.3%
H 146
 
0.1%
I 60
 
< 0.1%
J 33
 
< 0.1%
Other values (4) 23
 
< 0.1%

cat104
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
E
29544 
G
27805 
D
18804 
F
13232 
H
11619 
Other values (12)
28442 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowG
2nd rowG
3rd rowD
4th rowE
5th rowF

Common Values

ValueCountFrequency (%)
E 29544
22.8%
G 27805
21.5%
D 18804
14.5%
F 13232
10.2%
H 11619
 
9.0%
K 10240
 
7.9%
I 7667
 
5.9%
C 4771
 
3.7%
L 2343
 
1.8%
J 2146
 
1.7%
Other values (7) 1275
 
1.0%

Length

2023-09-24T16:01:17.801858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e 29544
22.8%
g 27805
21.5%
d 18804
14.5%
f 13232
10.2%
h 11619
 
9.0%
k 10240
 
7.9%
i 7667
 
5.9%
c 4771
 
3.7%
l 2343
 
1.8%
j 2146
 
1.7%
Other values (7) 1275
 
1.0%

Most occurring characters

ValueCountFrequency (%)
E 29544
22.8%
G 27805
21.5%
D 18804
14.5%
F 13232
10.2%
H 11619
 
9.0%
K 10240
 
7.9%
I 7667
 
5.9%
C 4771
 
3.7%
L 2343
 
1.8%
J 2146
 
1.7%
Other values (7) 1275
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 29544
22.8%
G 27805
21.5%
D 18804
14.5%
F 13232
10.2%
H 11619
 
9.0%
K 10240
 
7.9%
I 7667
 
5.9%
C 4771
 
3.7%
L 2343
 
1.8%
J 2146
 
1.7%
Other values (7) 1275
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 29544
22.8%
G 27805
21.5%
D 18804
14.5%
F 13232
10.2%
H 11619
 
9.0%
K 10240
 
7.9%
I 7667
 
5.9%
C 4771
 
3.7%
L 2343
 
1.8%
J 2146
 
1.7%
Other values (7) 1275
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 29544
22.8%
G 27805
21.5%
D 18804
14.5%
F 13232
10.2%
H 11619
 
9.0%
K 10240
 
7.9%
I 7667
 
5.9%
C 4771
 
3.7%
L 2343
 
1.8%
J 2146
 
1.7%
Other values (7) 1275
 
1.0%

cat105
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
E
52536 
F
43221 
G
14106 
D
8463 
H
7738 
Other values (13)
 
3382

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowE
2nd rowG
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 52536
40.6%
F 43221
33.4%
G 14106
 
10.9%
D 8463
 
6.5%
H 7738
 
6.0%
I 1986
 
1.5%
J 513
 
0.4%
K 368
 
0.3%
C 177
 
0.1%
M 125
 
0.1%
Other values (8) 213
 
0.2%

Length

2023-09-24T16:01:18.003394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e 52536
40.6%
f 43221
33.4%
g 14106
 
10.9%
d 8463
 
6.5%
h 7738
 
6.0%
i 1986
 
1.5%
j 513
 
0.4%
k 368
 
0.3%
c 177
 
0.1%
m 125
 
0.1%
Other values (8) 213
 
0.2%

Most occurring characters

ValueCountFrequency (%)
E 52536
40.6%
F 43221
33.4%
G 14106
 
10.9%
D 8463
 
6.5%
H 7738
 
6.0%
I 1986
 
1.5%
J 513
 
0.4%
K 368
 
0.3%
C 177
 
0.1%
M 125
 
0.1%
Other values (8) 213
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 52536
40.6%
F 43221
33.4%
G 14106
 
10.9%
D 8463
 
6.5%
H 7738
 
6.0%
I 1986
 
1.5%
J 513
 
0.4%
K 368
 
0.3%
C 177
 
0.1%
M 125
 
0.1%
Other values (8) 213
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 52536
40.6%
F 43221
33.4%
G 14106
 
10.9%
D 8463
 
6.5%
H 7738
 
6.0%
I 1986
 
1.5%
J 513
 
0.4%
K 368
 
0.3%
C 177
 
0.1%
M 125
 
0.1%
Other values (8) 213
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 52536
40.6%
F 43221
33.4%
G 14106
 
10.9%
D 8463
 
6.5%
H 7738
 
6.0%
I 1986
 
1.5%
J 513
 
0.4%
K 368
 
0.3%
C 177
 
0.1%
M 125
 
0.1%
Other values (8) 213
 
0.2%

cat106
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
G
31957 
H
25654 
F
25143 
I
14878 
J
12504 
Other values (13)
19310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowI
2nd rowG
3rd rowJ
4th rowI
5th rowG

Common Values

ValueCountFrequency (%)
G 31957
24.7%
H 25654
19.8%
F 25143
19.4%
I 14878
11.5%
J 12504
 
9.7%
E 9157
 
7.1%
K 5723
 
4.4%
L 1947
 
1.5%
D 1315
 
1.0%
M 833
 
0.6%
Other values (8) 335
 
0.3%

Length

2023-09-24T16:01:18.204626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g 31957
24.7%
h 25654
19.8%
f 25143
19.4%
i 14878
11.5%
j 12504
 
9.7%
e 9157
 
7.1%
k 5723
 
4.4%
l 1947
 
1.5%
d 1315
 
1.0%
m 833
 
0.6%
Other values (8) 335
 
0.3%

Most occurring characters

ValueCountFrequency (%)
G 31957
24.7%
H 25654
19.8%
F 25143
19.4%
I 14878
11.5%
J 12504
 
9.7%
E 9157
 
7.1%
K 5723
 
4.4%
L 1947
 
1.5%
D 1315
 
1.0%
M 833
 
0.6%
Other values (8) 335
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 31957
24.7%
H 25654
19.8%
F 25143
19.4%
I 14878
11.5%
J 12504
 
9.7%
E 9157
 
7.1%
K 5723
 
4.4%
L 1947
 
1.5%
D 1315
 
1.0%
M 833
 
0.6%
Other values (8) 335
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 31957
24.7%
H 25654
19.8%
F 25143
19.4%
I 14878
11.5%
J 12504
 
9.7%
E 9157
 
7.1%
K 5723
 
4.4%
L 1947
 
1.5%
D 1315
 
1.0%
M 833
 
0.6%
Other values (8) 335
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 31957
24.7%
H 25654
19.8%
F 25143
19.4%
I 14878
11.5%
J 12504
 
9.7%
E 9157
 
7.1%
K 5723
 
4.4%
L 1947
 
1.5%
D 1315
 
1.0%
M 833
 
0.6%
Other values (8) 335
 
0.3%

cat107
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
F
32530 
G
19241 
H
16145 
J
15496 
K
14094 
Other values (15)
31940 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowL
2nd rowF
3rd rowG
4th rowK
5th rowE

Common Values

ValueCountFrequency (%)
F 32530
25.1%
G 19241
14.9%
H 16145
12.5%
J 15496
12.0%
K 14094
10.9%
I 13768
10.6%
E 8413
 
6.5%
L 5050
 
3.9%
D 2231
 
1.7%
M 1432
 
1.1%
Other values (10) 1046
 
0.8%

Length

2023-09-24T16:01:18.403819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f 32530
25.1%
g 19241
14.9%
h 16145
12.5%
j 15496
12.0%
k 14094
10.9%
i 13768
10.6%
e 8413
 
6.5%
l 5050
 
3.9%
d 2231
 
1.7%
m 1432
 
1.1%
Other values (10) 1046
 
0.8%

Most occurring characters

ValueCountFrequency (%)
F 32530
25.1%
G 19241
14.9%
H 16145
12.5%
J 15496
12.0%
K 14094
10.9%
I 13768
10.6%
E 8413
 
6.5%
L 5050
 
3.9%
D 2231
 
1.7%
M 1432
 
1.1%
Other values (10) 1046
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 32530
25.1%
G 19241
14.9%
H 16145
12.5%
J 15496
12.0%
K 14094
10.9%
I 13768
10.6%
E 8413
 
6.5%
L 5050
 
3.9%
D 2231
 
1.7%
M 1432
 
1.1%
Other values (10) 1046
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 32530
25.1%
G 19241
14.9%
H 16145
12.5%
J 15496
12.0%
K 14094
10.9%
I 13768
10.6%
E 8413
 
6.5%
L 5050
 
3.9%
D 2231
 
1.7%
M 1432
 
1.1%
Other values (10) 1046
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 32530
25.1%
G 19241
14.9%
H 16145
12.5%
J 15496
12.0%
K 14094
10.9%
I 13768
10.6%
E 8413
 
6.5%
L 5050
 
3.9%
D 2231
 
1.7%
M 1432
 
1.1%
Other values (10) 1046
 
0.8%

cat108
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
B
44556 
K
29658 
G
14906 
D
13176 
F
6946 
Other values (6)
20204 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK
2nd rowB
3rd rowA
4th rowK
5th rowB

Common Values

ValueCountFrequency (%)
B 44556
34.4%
K 29658
22.9%
G 14906
 
11.5%
D 13176
 
10.2%
F 6946
 
5.4%
A 6293
 
4.9%
E 5468
 
4.2%
I 4971
 
3.8%
H 2955
 
2.3%
C 379
 
0.3%

Length

2023-09-24T16:01:18.884121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b 44556
34.4%
k 29658
22.9%
g 14906
 
11.5%
d 13176
 
10.2%
f 6946
 
5.4%
a 6293
 
4.9%
e 5468
 
4.2%
i 4971
 
3.8%
h 2955
 
2.3%
c 379
 
0.3%

Most occurring characters

ValueCountFrequency (%)
B 44556
34.4%
K 29658
22.9%
G 14906
 
11.5%
D 13176
 
10.2%
F 6946
 
5.4%
A 6293
 
4.9%
E 5468
 
4.2%
I 4971
 
3.8%
H 2955
 
2.3%
C 379
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 44556
34.4%
K 29658
22.9%
G 14906
 
11.5%
D 13176
 
10.2%
F 6946
 
5.4%
A 6293
 
4.9%
E 5468
 
4.2%
I 4971
 
3.8%
H 2955
 
2.3%
C 379
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 44556
34.4%
K 29658
22.9%
G 14906
 
11.5%
D 13176
 
10.2%
F 6946
 
5.4%
A 6293
 
4.9%
E 5468
 
4.2%
I 4971
 
3.8%
H 2955
 
2.3%
C 379
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 44556
34.4%
K 29658
22.9%
G 14906
 
11.5%
D 13176
 
10.2%
F 6946
 
5.4%
A 6293
 
4.9%
E 5468
 
4.2%
I 4971
 
3.8%
H 2955
 
2.3%
C 379
 
0.3%

cat109
Text

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:19.135914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9665961
Min length1

Characters and Unicode

Total characters254568
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowBI
2nd rowBI
3rd rowBI
4th rowBI
5th rowAB
ValueCountFrequency (%)
bi 105348
81.4%
ab 14778
 
11.4%
bu 2228
 
1.7%
k 2020
 
1.6%
g 949
 
0.7%
bq 737
 
0.6%
n 308
 
0.2%
m 299
 
0.2%
bo 231
 
0.2%
d 172
 
0.1%
Other values (64) 2376
 
1.8%
2023-09-24T16:01:19.619108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 123750
48.6%
I 105524
41.5%
A 15711
 
6.2%
U 2291
 
0.9%
K 2038
 
0.8%
G 955
 
0.4%
Q 817
 
0.3%
C 475
 
0.2%
L 419
 
0.2%
N 325
 
0.1%
Other values (17) 2263
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 254433
99.9%
Space Separator 135
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 123750
48.6%
I 105524
41.5%
A 15711
 
6.2%
U 2291
 
0.9%
K 2038
 
0.8%
G 955
 
0.4%
Q 817
 
0.3%
C 475
 
0.2%
L 419
 
0.2%
N 325
 
0.1%
Other values (16) 2128
 
0.8%
Space Separator
ValueCountFrequency (%)
135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 254433
99.9%
Common 135
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 123750
48.6%
I 105524
41.5%
A 15711
 
6.2%
U 2291
 
0.9%
K 2038
 
0.8%
G 955
 
0.4%
Q 817
 
0.3%
C 475
 
0.2%
L 419
 
0.2%
N 325
 
0.1%
Other values (16) 2128
 
0.8%
Common
ValueCountFrequency (%)
135
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 254568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 123750
48.6%
I 105524
41.5%
A 15711
 
6.2%
U 2291
 
0.9%
K 2038
 
0.8%
G 955
 
0.4%
Q 817
 
0.3%
C 475
 
0.2%
L 419
 
0.2%
N 325
 
0.1%
Other values (17) 2263
 
0.9%

cat110
Text

Distinct135
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:20.049185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9456839
Min length1

Characters and Unicode

Total characters251861
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowBC
2nd rowCO
3rd rowCS
4th rowCR
5th rowEG
ValueCountFrequency (%)
cl 17319
13.4%
cs 16923
13.1%
eg 16733
12.9%
eb 15016
11.6%
co 12079
9.3%
bt 11254
8.7%
el 6169
 
4.8%
bc 2802
 
2.2%
dw 2438
 
1.9%
cq 2231
 
1.7%
Other values (113) 26482
20.5%
2023-09-24T16:01:20.710789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 58366
23.2%
E 40784
16.2%
B 32760
13.0%
L 23862
9.5%
S 18265
 
7.3%
G 17010
 
6.8%
O 12201
 
4.8%
T 11668
 
4.6%
D 7708
 
3.1%
A 6720
 
2.7%
Other values (16) 22517
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 251674
99.9%
Space Separator 187
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 58366
23.2%
E 40784
16.2%
B 32760
13.0%
L 23862
9.5%
S 18265
 
7.3%
G 17010
 
6.8%
O 12201
 
4.8%
T 11668
 
4.6%
D 7708
 
3.1%
A 6720
 
2.7%
Other values (15) 22330
 
8.9%
Space Separator
ValueCountFrequency (%)
187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 251674
99.9%
Common 187
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 58366
23.2%
E 40784
16.2%
B 32760
13.0%
L 23862
9.5%
S 18265
 
7.3%
G 17010
 
6.8%
O 12201
 
4.8%
T 11668
 
4.6%
D 7708
 
3.1%
A 6720
 
2.7%
Other values (15) 22330
 
8.9%
Common
ValueCountFrequency (%)
187
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 251861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 58366
23.2%
E 40784
16.2%
B 32760
13.0%
L 23862
9.5%
S 18265
 
7.3%
G 17010
 
6.8%
O 12201
 
4.8%
T 11668
 
4.6%
D 7708
 
3.1%
A 6720
 
2.7%
Other values (16) 22517
 
8.9%

cat111
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
86026 
C
21323 
E
9793 
G
 
4560
A
 
2694
Other values (22)
 
5050

Length

Max length2
Median length1
Mean length1.0301284
Min length1

Characters and Unicode

Total characters133346
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowE
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 86026
66.5%
C 21323
 
16.5%
E 9793
 
7.6%
G 4560
 
3.5%
A 2694
 
2.1%
I 2473
 
1.9%
K 824
 
0.6%
C 643
 
0.5%
E 318
 
0.2%
M 311
 
0.2%
Other values (17) 481
 
0.4%

Length

2023-09-24T16:01:20.949502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 88720
68.5%
c 21966
 
17.0%
e 10111
 
7.8%
g 4679
 
3.6%
i 2555
 
2.0%
k 852
 
0.7%
m 317
 
0.2%
o 130
 
0.1%
q 55
 
< 0.1%
s 31
 
< 0.1%
Other values (6) 30
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 88720
66.5%
C 21966
 
16.5%
E 10111
 
7.6%
G 4679
 
3.5%
3900
 
2.9%
I 2555
 
1.9%
K 852
 
0.6%
M 317
 
0.2%
O 130
 
0.1%
Q 55
 
< 0.1%
Other values (7) 61
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
97.1%
Space Separator 3900
 
2.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 88720
68.5%
C 21966
 
17.0%
E 10111
 
7.8%
G 4679
 
3.6%
I 2555
 
2.0%
K 852
 
0.7%
M 317
 
0.2%
O 130
 
0.1%
Q 55
 
< 0.1%
S 31
 
< 0.1%
Other values (6) 30
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3900
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
97.1%
Common 3900
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 88720
68.5%
C 21966
 
17.0%
E 10111
 
7.8%
G 4679
 
3.6%
I 2555
 
2.0%
K 852
 
0.7%
M 317
 
0.2%
O 130
 
0.1%
Q 55
 
< 0.1%
S 31
 
< 0.1%
Other values (6) 30
 
< 0.1%
Common
ValueCountFrequency (%)
3900
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 88720
66.5%
C 21966
 
16.5%
E 10111
 
7.6%
G 4679
 
3.5%
3900
 
2.9%
I 2555
 
1.9%
K 852
 
0.6%
M 317
 
0.2%
O 130
 
0.1%
Q 55
 
< 0.1%
Other values (7) 61
 
< 0.1%

cat112
Text

Distinct76
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:21.235559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.5227508
Min length1

Characters and Unicode

Total characters197114
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJ
2nd rowG
3rd rowU
4th rowAY
5th rowE
ValueCountFrequency (%)
e 17473
13.5%
ah 12886
 
10.0%
as 12018
 
9.3%
j 11209
 
8.7%
an 6363
 
4.9%
af 6321
 
4.9%
u 5715
 
4.4%
n 5658
 
4.4%
av 4985
 
3.9%
ak 4481
 
3.5%
Other values (41) 42337
32.7%
2023-09-24T16:01:21.755611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 68351
34.7%
E 18027
 
9.1%
S 15021
 
7.6%
H 13251
 
6.7%
N 12021
 
6.1%
J 11302
 
5.7%
K 8657
 
4.4%
F 8460
 
4.3%
U 6003
 
3.0%
V 5427
 
2.8%
Other values (16) 30594
15.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 195190
99.0%
Space Separator 1924
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 68351
35.0%
E 18027
 
9.2%
S 15021
 
7.7%
H 13251
 
6.8%
N 12021
 
6.2%
J 11302
 
5.8%
K 8657
 
4.4%
F 8460
 
4.3%
U 6003
 
3.1%
V 5427
 
2.8%
Other values (15) 28670
14.7%
Space Separator
ValueCountFrequency (%)
1924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 195190
99.0%
Common 1924
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 68351
35.0%
E 18027
 
9.2%
S 15021
 
7.7%
H 13251
 
6.8%
N 12021
 
6.2%
J 11302
 
5.8%
K 8657
 
4.4%
F 8460
 
4.3%
U 6003
 
3.1%
V 5427
 
2.8%
Other values (15) 28670
14.7%
Common
ValueCountFrequency (%)
1924
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 68351
34.7%
E 18027
 
9.1%
S 15021
 
7.6%
H 13251
 
6.7%
N 12021
 
6.1%
J 11302
 
5.7%
K 8657
 
4.4%
F 8460
 
4.3%
U 6003
 
3.0%
V 5427
 
2.8%
Other values (16) 30594
15.5%

cat113
Text

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:22.068578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.6348902
Min length1

Characters and Unicode

Total characters211630
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowAX
2nd rowX
3rd rowAE
4th rowAJ
5th rowI
ValueCountFrequency (%)
bm 18089
 
14.0%
ae 15367
 
11.9%
l 9073
 
7.0%
ax 8684
 
6.7%
y 7982
 
6.2%
k 5194
 
4.0%
x 4799
 
3.7%
s 4742
 
3.7%
af 4083
 
3.2%
an 3591
 
2.8%
Other values (50) 47842
37.0%
2023-09-24T16:01:22.573925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 54876
25.9%
B 28157
13.3%
M 20152
 
9.5%
E 15369
 
7.3%
X 13483
 
6.4%
L 9077
 
4.3%
Y 8294
 
3.9%
K 8063
 
3.8%
N 7962
 
3.8%
S 7421
 
3.5%
Other values (16) 38776
18.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 210185
99.3%
Space Separator 1445
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 54876
26.1%
B 28157
13.4%
M 20152
 
9.6%
E 15369
 
7.3%
X 13483
 
6.4%
L 9077
 
4.3%
Y 8294
 
3.9%
K 8063
 
3.8%
N 7962
 
3.8%
S 7421
 
3.5%
Other values (15) 37331
17.8%
Space Separator
ValueCountFrequency (%)
1445
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 210185
99.3%
Common 1445
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 54876
26.1%
B 28157
13.4%
M 20152
 
9.6%
E 15369
 
7.3%
X 13483
 
6.4%
L 9077
 
4.3%
Y 8294
 
3.9%
K 8063
 
3.8%
N 7962
 
3.8%
S 7421
 
3.5%
Other values (15) 37331
17.8%
Common
ValueCountFrequency (%)
1445
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 54876
25.9%
B 28157
13.3%
M 20152
 
9.5%
E 15369
 
7.3%
X 13483
 
6.4%
L 9077
 
4.3%
Y 8294
 
3.9%
K 8063
 
3.8%
N 7962
 
3.8%
S 7421
 
3.5%
Other values (16) 38776
18.3%

cat114
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
A
90742 
E
11342 
C
11306 
F
 
5712
J
 
5531
Other values (13)
 
4813

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowC

Common Values

ValueCountFrequency (%)
A 90742
70.1%
E 11342
 
8.8%
C 11306
 
8.7%
F 5712
 
4.4%
J 5531
 
4.3%
N 1697
 
1.3%
I 1621
 
1.3%
L 564
 
0.4%
R 562
 
0.4%
O 159
 
0.1%
Other values (8) 210
 
0.2%

Length

2023-09-24T16:01:22.822704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 90742
70.1%
e 11342
 
8.8%
c 11306
 
8.7%
f 5712
 
4.4%
j 5531
 
4.3%
n 1697
 
1.3%
i 1621
 
1.3%
l 564
 
0.4%
r 562
 
0.4%
o 159
 
0.1%
Other values (8) 210
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 90742
70.1%
E 11342
 
8.8%
C 11306
 
8.7%
F 5712
 
4.4%
J 5531
 
4.3%
N 1697
 
1.3%
I 1621
 
1.3%
L 564
 
0.4%
R 562
 
0.4%
O 159
 
0.1%
Other values (8) 210
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 90742
70.1%
E 11342
 
8.8%
C 11306
 
8.7%
F 5712
 
4.4%
J 5531
 
4.3%
N 1697
 
1.3%
I 1621
 
1.3%
L 564
 
0.4%
R 562
 
0.4%
O 159
 
0.1%
Other values (8) 210
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 90742
70.1%
E 11342
 
8.8%
C 11306
 
8.7%
F 5712
 
4.4%
J 5531
 
4.3%
N 1697
 
1.3%
I 1621
 
1.3%
L 564
 
0.4%
R 562
 
0.4%
O 159
 
0.1%
Other values (8) 210
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 90742
70.1%
E 11342
 
8.8%
C 11306
 
8.7%
F 5712
 
4.4%
J 5531
 
4.3%
N 1697
 
1.3%
I 1621
 
1.3%
L 564
 
0.4%
R 562
 
0.4%
O 159
 
0.1%
Other values (8) 210
 
0.2%

cat115
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
K
30054 
O
18473 
J
16234 
N
15446 
P
15331 
Other values (18)
33908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129446
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowQ
2nd rowL
3rd rowK
4th rowP
5th rowJ

Common Values

ValueCountFrequency (%)
K 30054
23.2%
O 18473
14.3%
J 16234
12.5%
N 15446
11.9%
P 15331
11.8%
L 10833
 
8.4%
M 8544
 
6.6%
Q 5624
 
4.3%
I 4754
 
3.7%
H 1975
 
1.5%
Other values (13) 2178
 
1.7%

Length

2023-09-24T16:01:23.034160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
k 30054
23.2%
o 18473
14.3%
j 16234
12.5%
n 15446
11.9%
p 15331
11.8%
l 10833
 
8.4%
m 8544
 
6.6%
q 5624
 
4.3%
i 4754
 
3.7%
h 1975
 
1.5%
Other values (13) 2178
 
1.7%

Most occurring characters

ValueCountFrequency (%)
K 30054
23.2%
O 18473
14.3%
J 16234
12.5%
N 15446
11.9%
P 15331
11.8%
L 10833
 
8.4%
M 8544
 
6.6%
Q 5624
 
4.3%
I 4754
 
3.7%
H 1975
 
1.5%
Other values (13) 2178
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 30054
23.2%
O 18473
14.3%
J 16234
12.5%
N 15446
11.9%
P 15331
11.8%
L 10833
 
8.4%
M 8544
 
6.6%
Q 5624
 
4.3%
I 4754
 
3.7%
H 1975
 
1.5%
Other values (13) 2178
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 129446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 30054
23.2%
O 18473
14.3%
J 16234
12.5%
N 15446
11.9%
P 15331
11.8%
L 10833
 
8.4%
M 8544
 
6.6%
Q 5624
 
4.3%
I 4754
 
3.7%
H 1975
 
1.5%
Other values (13) 2178
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 30054
23.2%
O 18473
14.3%
J 16234
12.5%
N 15446
11.9%
P 15331
11.8%
L 10833
 
8.4%
M 8544
 
6.6%
Q 5624
 
4.3%
I 4754
 
3.7%
H 1975
 
1.5%
Other values (13) 2178
 
1.7%

cat116
Text

Distinct314
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:23.619822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9983854
Min length1

Characters and Unicode

Total characters258683
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)< 0.1%

Sample

1st rowHG
2nd rowHK
3rd rowCK
4th rowDJ
5th rowHA
ValueCountFrequency (%)
hk 14465
 
11.2%
dj 14162
 
10.9%
ck 6876
 
5.3%
dp 6407
 
4.9%
gs 6107
 
4.7%
cr 4705
 
3.6%
hx 3794
 
2.9%
dc 3075
 
2.4%
hg 3043
 
2.4%
ie 2971
 
2.3%
Other values (301) 63841
49.3%
2023-09-24T16:01:24.454810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
H 35505
13.7%
D 29816
11.5%
K 29587
11.4%
C 25622
9.9%
G 20195
 
7.8%
J 18198
 
7.0%
L 14362
 
5.6%
I 9805
 
3.8%
B 7701
 
3.0%
P 7293
 
2.8%
Other values (16) 60599
23.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 258675
> 99.9%
Space Separator 8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 35505
13.7%
D 29816
11.5%
K 29587
11.4%
C 25622
9.9%
G 20195
 
7.8%
J 18198
 
7.0%
L 14362
 
5.6%
I 9805
 
3.8%
B 7701
 
3.0%
P 7293
 
2.8%
Other values (15) 60591
23.4%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 258675
> 99.9%
Common 8
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 35505
13.7%
D 29816
11.5%
K 29587
11.4%
C 25622
9.9%
G 20195
 
7.8%
J 18198
 
7.0%
L 14362
 
5.6%
I 9805
 
3.8%
B 7701
 
3.0%
P 7293
 
2.8%
Other values (15) 60591
23.4%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 35505
13.7%
D 29816
11.5%
K 29587
11.4%
C 25622
9.9%
G 20195
 
7.8%
J 18198
 
7.0%
L 14362
 
5.6%
I 9805
 
3.8%
B 7701
 
3.0%
P 7293
 
2.8%
Other values (16) 60599
23.4%

cont1
Real number (ℝ)

HIGH CORRELATION 

Distinct629
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49445674
Minimum1.6 × 10-5
Maximum0.984975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:24.729773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6 × 10-5
5-th percentile0.230658
Q10.347403
median0.475784
Q30.62663
95-th percentile0.893784
Maximum0.984975
Range0.984959
Interquartile range (IQR)0.279227

Descriptive statistics

Standard deviation0.18801772
Coefficient of variation (CV)0.3802511
Kurtosis-0.11989775
Mean0.49445674
Median Absolute Deviation (MAD)0.137181
Skewness0.5077178
Sum64005.447
Variance0.035350663
MonotonicityNot monotonic
2023-09-24T16:01:24.980360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.475784 3997
 
3.1%
0.503312 2218
 
1.7%
0.54667 2092
 
1.6%
0.325401 2079
 
1.6%
0.484469 1825
 
1.4%
0.330514 1773
 
1.4%
0.480125 1755
 
1.4%
0.894333 1569
 
1.2%
0.465671 1451
 
1.1%
0.629339 1395
 
1.1%
Other values (619) 109292
84.4%
ValueCountFrequency (%)
1.6 × 10-5144
0.1%
0.007274 23
 
< 0.1%
0.016235 1
 
< 0.1%
0.048643 9
 
< 0.1%
0.057987 107
0.1%
0.058944 9
 
< 0.1%
0.059591 1
 
< 0.1%
0.061571 15
 
< 0.1%
0.06752 124
0.1%
0.071264 43
 
< 0.1%
ValueCountFrequency (%)
0.984975 1
 
< 0.1%
0.984452 4
 
< 0.1%
0.981552 11
 
< 0.1%
0.980802 24
< 0.1%
0.980582 3
 
< 0.1%
0.977873 57
< 0.1%
0.977747 26
< 0.1%
0.977365 1
 
< 0.1%
0.976847 1
 
< 0.1%
0.970149 2
 
< 0.1%

cont2
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5070451
Minimum0.001149
Maximum0.862654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:25.205186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001149
5-th percentile0.15999
Q10.358319
median0.555782
Q30.681761
95-th percentile0.785784
Maximum0.862654
Range0.861505
Interquartile range (IQR)0.323442

Descriptive statistics

Standard deviation0.20685113
Coefficient of variation (CV)0.40795411
Kurtosis-0.88729426
Mean0.5070451
Median Absolute Deviation (MAD)0.181286
Skewness-0.31202666
Sum65634.96
Variance0.042787392
MonotonicityNot monotonic
2023-09-24T16:01:25.438516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.555782 14517
11.2%
0.620805 13883
10.7%
0.488789 13142
10.2%
0.737068 11889
9.2%
0.422197 11559
8.9%
0.785784 10724
8.3%
0.681761 10027
7.7%
0.358319 9926
7.7%
0.299102 7971
6.2%
0.245921 6385
 
4.9%
Other values (23) 19423
15.0%
ValueCountFrequency (%)
0.001149 4
 
< 0.1%
0.001503 5
 
< 0.1%
0.001966 4
 
< 0.1%
0.002571 8
 
< 0.1%
0.003362 13
 
< 0.1%
0.004394 15
 
< 0.1%
0.005742 21
< 0.1%
0.007501 19
< 0.1%
0.009792 32
< 0.1%
0.012775 46
< 0.1%
ValueCountFrequency (%)
0.862654 121
 
0.1%
0.827585 4730
 
3.7%
0.785784 10724
8.3%
0.737068 11889
9.2%
0.681761 10027
7.7%
0.620805 13883
10.7%
0.555782 14517
11.2%
0.488789 13142
10.2%
0.422197 11559
8.9%
0.358319 9926
7.7%

cont3
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49832374
Minimum0.002634
Maximum0.944251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:25.671863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002634
5-th percentile0.174588
Q10.336963
median0.527991
Q30.634224
95-th percentile0.819748
Maximum0.944251
Range0.941617
Interquartile range (IQR)0.297261

Descriptive statistics

Standard deviation0.20176735
Coefficient of variation (CV)0.40489211
Kurtosis-0.6021858
Mean0.49832374
Median Absolute Deviation (MAD)0.14587
Skewness-0.0032108439
Sum64506.014
Variance0.040710064
MonotonicityNot monotonic
2023-09-24T16:01:25.915274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54977 11439
 
8.8%
0.484196 6440
 
5.0%
0.506105 6413
 
5.0%
0.527991 5666
 
4.4%
0.336963 5292
 
4.1%
0.61366 5037
 
3.9%
0.692825 4858
 
3.8%
0.440642 4770
 
3.7%
0.592681 4570
 
3.5%
0.57136 4082
 
3.2%
Other values (67) 70879
54.8%
ValueCountFrequency (%)
0.002634 52
 
< 0.1%
0.01945 2
 
< 0.1%
0.025153 4
 
< 0.1%
0.027395 1
 
< 0.1%
0.02983 3
 
< 0.1%
0.032474 27
 
< 0.1%
0.035344 6
 
< 0.1%
0.038457 19
 
< 0.1%
0.041833 168
0.1%
0.045491 4
 
< 0.1%
ValueCountFrequency (%)
0.944251 3193
2.5%
0.939453 311
 
0.2%
0.93427 2
 
< 0.1%
0.928678 25
 
< 0.1%
0.922649 46
 
< 0.1%
0.916157 75
 
0.1%
0.909173 353
 
0.3%
0.90167 89
 
0.1%
0.89362 81
 
0.1%
0.884994 154
 
0.1%

cont4
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49219143
Minimum0.176921
Maximum0.956046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:26.142529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.176921
5-th percentile0.208655
Q10.327354
median0.452887
Q30.652072
95-th percentile0.864586
Maximum0.956046
Range0.779125
Interquartile range (IQR)0.324718

Descriptive statistics

Standard deviation0.2107973
Coefficient of variation (CV)0.42828315
Kurtosis-0.95252
Mean0.49219143
Median Absolute Deviation (MAD)0.161247
Skewness0.42114459
Sum63712.211
Variance0.0444355
MonotonicityNot monotonic
2023-09-24T16:01:26.393658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.373816 6862
 
5.3%
0.473202 5420
 
4.2%
0.327354 4673
 
3.6%
0.309621 4071
 
3.1%
0.452887 3627
 
2.8%
0.432728 3551
 
2.7%
0.412789 3273
 
2.5%
0.534409 3188
 
2.5%
0.208655 3088
 
2.4%
0.62377 2981
 
2.3%
Other values (102) 88712
68.5%
ValueCountFrequency (%)
0.176921 762
 
0.6%
0.18295 1274
1.0%
0.189137 2178
1.7%
0.195483 1405
1.1%
0.201989 331
 
0.3%
0.208655 3088
2.4%
0.215482 1996
1.5%
0.222469 1241
1.0%
0.229617 2376
1.8%
0.236924 1535
1.2%
ValueCountFrequency (%)
0.956046 1
 
< 0.1%
0.954297 1
 
< 0.1%
0.952482 72
 
0.1%
0.950598 17
 
< 0.1%
0.948643 467
0.4%
0.946616 191
 
0.1%
0.944513 328
0.3%
0.942332 52
 
< 0.1%
0.940071 9
 
< 0.1%
0.935299 492
0.4%

cont5
Real number (ℝ)

Distinct139
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48744676
Minimum0.281143
Maximum0.983107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:26.627205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.281143
5-th percentile0.281143
Q10.281143
median0.422268
Q30.643315
95-th percentile0.882274
Maximum0.983107
Range0.701964
Interquartile range (IQR)0.362172

Descriptive statistics

Standard deviation0.20906123
Coefficient of variation (CV)0.4288904
Kurtosis-0.88586496
Mean0.48744676
Median Absolute Deviation (MAD)0.141125
Skewness0.67847254
Sum63098.033
Variance0.043706599
MonotonicityNot monotonic
2023-09-24T16:01:26.883585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.281143 35869
27.7%
0.422268 6270
 
4.8%
0.302678 5392
 
4.2%
0.372405 3303
 
2.6%
0.594196 2571
 
2.0%
0.78323 2406
 
1.9%
0.551723 2369
 
1.8%
0.491114 2160
 
1.7%
0.534484 2153
 
1.7%
0.388783 2126
 
1.6%
Other values (129) 64827
50.1%
ValueCountFrequency (%)
0.281143 35869
27.7%
0.288217 1469
 
1.1%
0.295397 1821
 
1.4%
0.302678 5392
 
4.2%
0.310061 1047
 
0.8%
0.317541 500
 
0.4%
0.325117 378
 
0.3%
0.332785 424
 
0.3%
0.340543 1106
 
0.9%
0.348388 1419
 
1.1%
ValueCountFrequency (%)
0.983107 1
 
< 0.1%
0.98252 29
< 0.1%
0.981914 3
 
< 0.1%
0.981286 27
< 0.1%
0.980637 2
 
< 0.1%
0.979967 6
 
< 0.1%
0.978556 1
 
< 0.1%
0.976257 1
 
< 0.1%
0.975438 4
 
< 0.1%
0.973717 14
< 0.1%

cont6
Real number (ℝ)

HIGH CORRELATION 

Distinct2457
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49229731
Minimum0.012683
Maximum0.997162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:27.124191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.012683
5-th percentile0.206854
Q10.336105
median0.441525
Q30.659261
95-th percentile0.868102
Maximum0.997162
Range0.984479
Interquartile range (IQR)0.323156

Descriptive statistics

Standard deviation0.20559897
Coefficient of variation (CV)0.41763172
Kurtosis-0.77916012
Mean0.49229731
Median Absolute Deviation (MAD)0.140406
Skewness0.45379667
Sum63725.917
Variance0.042270936
MonotonicityNot monotonic
2023-09-24T16:01:27.375851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3735 2022
 
1.6%
0.364464 1796
 
1.4%
0.240069 1391
 
1.1%
0.201125 1093
 
0.8%
0.317735 984
 
0.8%
0.379574 769
 
0.6%
0.220686 762
 
0.6%
0.220282 725
 
0.6%
0.252511 719
 
0.6%
0.336105 695
 
0.5%
Other values (2447) 118490
91.5%
ValueCountFrequency (%)
0.012683 139
0.1%
0.08608 1
 
< 0.1%
0.096818 1
 
< 0.1%
0.09723 19
 
< 0.1%
0.101868 4
 
< 0.1%
0.115885 1
 
< 0.1%
0.116368 3
 
< 0.1%
0.116974 1
 
< 0.1%
0.117583 3
 
< 0.1%
0.119425 1
 
< 0.1%
ValueCountFrequency (%)
0.997162 4
< 0.1%
0.996786 1
 
< 0.1%
0.989024 3
 
< 0.1%
0.98896 8
< 0.1%
0.987556 1
 
< 0.1%
0.987527 2
 
< 0.1%
0.98713 1
 
< 0.1%
0.986812 8
< 0.1%
0.986297 2
 
< 0.1%
0.98318 4
< 0.1%

cont7
Real number (ℝ)

HIGH CORRELATION 

Distinct5279
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48604796
Minimum0.069503
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:27.612173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.069503
5-th percentile0.262345
Q10.352087
median0.439379
Q30.591284
95-th percentile0.851333
Maximum1
Range0.930497
Interquartile range (IQR)0.239197

Descriptive statistics

Standard deviation0.17871202
Coefficient of variation (CV)0.36768391
Kurtosis0.045334279
Mean0.48604796
Median Absolute Deviation (MAD)0.109794
Skewness0.82538015
Sum62916.964
Variance0.031937985
MonotonicityNot monotonic
2023-09-24T16:01:27.865338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.347485 764
 
0.6%
0.381883 729
 
0.6%
0.350175 725
 
0.6%
0.283691 645
 
0.5%
0.39101 570
 
0.4%
0.362399 556
 
0.4%
0.401162 543
 
0.4%
0.359439 482
 
0.4%
0.360121 475
 
0.4%
0.279895 449
 
0.3%
Other values (5269) 123508
95.4%
ValueCountFrequency (%)
0.069503 195
0.2%
0.137667 1
 
< 0.1%
0.13928 1
 
< 0.1%
0.144178 1
 
< 0.1%
0.145621 1
 
< 0.1%
0.146101 1
 
< 0.1%
0.147995 1
 
< 0.1%
0.148606 1
 
< 0.1%
0.153863 2
 
< 0.1%
0.154481 1
 
< 0.1%
ValueCountFrequency (%)
1 17
< 0.1%
0.999999 5
 
< 0.1%
0.999997 3
 
< 0.1%
0.999988 2
 
< 0.1%
0.999987 1
 
< 0.1%
0.999986 2
 
< 0.1%
0.999981 1
 
< 0.1%
0.999975 3
 
< 0.1%
0.99994 1
 
< 0.1%
0.999936 1
 
< 0.1%

cont8
Real number (ℝ)

HIGH CORRELATION 

Distinct198
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48734554
Minimum0.23688
Maximum0.9828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:28.375496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.23688
5-th percentile0.24564
Q10.31796
median0.44106
Q30.62918
95-th percentile0.88693
Maximum0.9828
Range0.74592
Interquartile range (IQR)0.31122

Descriptive statistics

Standard deviation0.19955964
Coefficient of variation (CV)0.40948284
Kurtosis-0.56069386
Mean0.48734554
Median Absolute Deviation (MAD)0.14348
Skewness0.66853401
Sum63084.931
Variance0.039824049
MonotonicityNot monotonic
2023-09-24T16:01:28.640093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26847 7720
 
6.0%
0.36083 6381
 
4.9%
0.24564 5568
 
4.3%
0.29758 3136
 
2.4%
0.27797 3084
 
2.4%
0.58354 2827
 
2.2%
0.33906 2725
 
2.1%
0.34987 2369
 
1.8%
0.31796 2138
 
1.7%
0.3128 1887
 
1.5%
Other values (188) 91611
70.8%
ValueCountFrequency (%)
0.23688 695
 
0.5%
0.24123 1059
 
0.8%
0.24564 5568
4.3%
0.2501 424
 
0.3%
0.25461 1292
 
1.0%
0.25918 1314
 
1.0%
0.2638 730
 
0.6%
0.26847 7720
6.0%
0.2732 993
 
0.8%
0.27797 3084
 
2.4%
ValueCountFrequency (%)
0.9828 2
 
< 0.1%
0.9802 212
0.2%
0.97973 322
0.2%
0.97925 18
 
< 0.1%
0.97875 4
 
< 0.1%
0.97774 1
 
< 0.1%
0.97556 58
 
< 0.1%
0.97498 100
 
0.1%
0.97253 62
 
< 0.1%
0.97189 99
 
0.1%

cont9
Real number (ℝ)

HIGH CORRELATION 

Distinct340
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48603818
Minimum8 × 10-5
Maximum0.9954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:28.892746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8 × 10-5
5-th percentile0.27393
Q10.35897
median0.44145
Q30.56889
95-th percentile0.91644
Maximum0.9954
Range0.99532
Interquartile range (IQR)0.20992

Descriptive statistics

Standard deviation0.18213951
Coefficient of variation (CV)0.37474322
Kurtosis0.53626789
Mean0.48603818
Median Absolute Deviation (MAD)0.0942
Skewness1.0597154
Sum62915.698
Variance0.033174803
MonotonicityNot monotonic
2023-09-24T16:01:29.149639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.38249 5919
 
4.6%
0.44352 4570
 
3.5%
0.30859 4447
 
3.4%
0.32128 4322
 
3.3%
0.41471 3668
 
2.8%
0.46226 3119
 
2.4%
0.39447 3095
 
2.4%
0.39849 3003
 
2.3%
0.5042 2684
 
2.1%
0.34365 2681
 
2.1%
Other values (330) 91938
71.0%
ValueCountFrequency (%)
8 × 10-5124
0.1%
0.03786 23
 
< 0.1%
0.05742 1
 
< 0.1%
0.10579 5
 
< 0.1%
0.12278 2
 
< 0.1%
0.12831 77
0.1%
0.1302 5
 
< 0.1%
0.13116 2
 
< 0.1%
0.14103 2
 
< 0.1%
0.14206 9
 
< 0.1%
ValueCountFrequency (%)
0.9954 1
 
< 0.1%
0.99379 5
 
< 0.1%
0.99203 2
 
< 0.1%
0.99169 9
 
< 0.1%
0.9914 4
 
< 0.1%
0.99133 11
 
< 0.1%
0.99026 110
0.1%
0.98811 26
 
< 0.1%
0.9833 62
< 0.1%
0.9826 11
 
< 0.1%

cont10
Real number (ℝ)

HIGH CORRELATION 

Distinct169
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49901722
Minimum0
Maximum0.99498
Zeros146
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:29.387449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21983
Q10.36458
median0.46672
Q30.61984
95-th percentile0.81923
Maximum0.99498
Range0.99498
Interquartile range (IQR)0.25526

Descriptive statistics

Standard deviation0.18595589
Coefficient of variation (CV)0.37264424
Kurtosis-0.86250132
Mean0.49901722
Median Absolute Deviation (MAD)0.13197
Skewness0.34708328
Sum64595.783
Variance0.034579594
MonotonicityNot monotonic
2023-09-24T16:01:29.648782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.43919 4928
 
3.8%
0.45017 4854
 
3.7%
0.8351 4043
 
3.1%
0.47779 3325
 
2.6%
0.39599 3296
 
2.5%
0.50556 3257
 
2.5%
0.44467 2992
 
2.3%
0.2123 2984
 
2.3%
0.36458 2793
 
2.2%
0.37493 2659
 
2.1%
Other values (159) 94315
72.9%
ValueCountFrequency (%)
0 146
0.1%
0.07664 23
 
< 0.1%
0.09975 1
 
< 0.1%
0.10381 1
 
< 0.1%
0.12395 3
 
< 0.1%
0.13137 11
 
< 0.1%
0.13917 2
 
< 0.1%
0.14458 1
 
< 0.1%
0.14735 4
 
< 0.1%
0.15016 16
 
< 0.1%
ValueCountFrequency (%)
0.99498 3
 
< 0.1%
0.99475 3
 
< 0.1%
0.99463 40
< 0.1%
0.99451 20
 
< 0.1%
0.99048 29
< 0.1%
0.95454 4
 
< 0.1%
0.87114 34
< 0.1%
0.85815 2
 
< 0.1%
0.84698 1
 
< 0.1%
0.84408 56
< 0.1%

cont11
Real number (ℝ)

HIGH CORRELATION 

Distinct314
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4944418
Minimum0.035321
Maximum0.99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:29.892227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.035321
5-th percentile0.199654
Q10.314313
median0.457203
Q30.678924
95-th percentile0.837272
Maximum0.99783
Range0.962509
Interquartile range (IQR)0.364611

Descriptive statistics

Standard deviation0.21041116
Coefficient of variation (CV)0.42555294
Kurtosis-1.0498056
Mean0.4944418
Median Absolute Deviation (MAD)0.168733
Skewness0.28158747
Sum64003.513
Variance0.044272857
MonotonicityNot monotonic
2023-09-24T16:01:30.150780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.341813 4914
 
3.8%
0.453334 4573
 
3.5%
0.4922 4202
 
3.2%
0.24541 4016
 
3.1%
0.644013 3018
 
2.3%
0.307628 2889
 
2.2%
0.415029 2836
 
2.2%
0.6075 2697
 
2.1%
0.569745 2636
 
2.0%
0.797841 2599
 
2.0%
Other values (304) 95066
73.4%
ValueCountFrequency (%)
0.035321 48
< 0.1%
0.038077 1
 
< 0.1%
0.07949 1
 
< 0.1%
0.085388 1
 
< 0.1%
0.089115 1
 
< 0.1%
0.094312 1
 
< 0.1%
0.098386 36
< 0.1%
0.099779 1
 
< 0.1%
0.110025 1
 
< 0.1%
0.113118 1
 
< 0.1%
ValueCountFrequency (%)
0.99783 2
 
< 0.1%
0.997796 4
 
< 0.1%
0.997762 1
 
< 0.1%
0.996082 1
 
< 0.1%
0.995831 1
 
< 0.1%
0.995352 7
< 0.1%
0.995279 8
< 0.1%
0.995054 15
< 0.1%
0.994898 2
 
< 0.1%
0.994313 1
 
< 0.1%

cont12
Real number (ℝ)

HIGH CORRELATION 

Distinct315
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49410437
Minimum0.036232
Maximum0.997416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:30.385660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.036232
5-th percentile0.204687
Q10.318249
median0.462286
Q30.682413
95-th percentile0.843026
Maximum0.997416
Range0.961184
Interquartile range (IQR)0.364164

Descriptive statistics

Standard deviation0.21015997
Coefficient of variation (CV)0.42533517
Kurtosis-1.0315984
Mean0.49410437
Median Absolute Deviation (MAD)0.168567
Skewness0.29200889
Sum63959.834
Variance0.044167211
MonotonicityNot monotonic
2023-09-24T16:01:30.641492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.443374 4890
 
3.8%
0.481306 4832
 
3.7%
0.241676 4127
 
3.2%
0.352251 4062
 
3.1%
0.301921 3249
 
2.5%
0.335036 3182
 
2.5%
0.594646 2724
 
2.1%
0.785706 2674
 
2.1%
0.714544 2615
 
2.0%
0.630853 2451
 
1.9%
Other values (305) 94640
73.1%
ValueCountFrequency (%)
0.036232 48
< 0.1%
0.038994 1
 
< 0.1%
0.080083 1
 
< 0.1%
0.088318 1
 
< 0.1%
0.089555 1
 
< 0.1%
0.09466 1
 
< 0.1%
0.098659 36
< 0.1%
0.108579 1
 
< 0.1%
0.114632 1
 
< 0.1%
0.116191 2
 
< 0.1%
ValueCountFrequency (%)
0.997416 2
 
< 0.1%
0.997376 4
< 0.1%
0.997336 1
 
< 0.1%
0.997211 6
< 0.1%
0.995393 1
 
< 0.1%
0.995104 1
 
< 0.1%
0.994555 7
< 0.1%
0.994472 8
< 0.1%
0.994214 9
< 0.1%
0.994036 3
 
< 0.1%

cont13
Real number (ℝ)

HIGH CORRELATION 

Distinct355
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49520612
Minimum0.000228
Maximum0.988494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:30.880277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.000228
5-th percentile0.250991
Q10.315758
median0.366638
Q30.689974
95-th percentile0.854872
Maximum0.988494
Range0.988266
Interquartile range (IQR)0.374216

Descriptive statistics

Standard deviation0.2130891
Coefficient of variation (CV)0.43030386
Kurtosis-1.3577421
Mean0.49520612
Median Absolute Deviation (MAD)0.120627
Skewness0.36863281
Sum64102.451
Variance0.045406966
MonotonicityNot monotonic
2023-09-24T16:01:31.126248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.339244 6084
 
4.7%
0.689974 3505
 
2.7%
0.30435 3256
 
2.5%
0.315758 3081
 
2.4%
0.333292 2969
 
2.3%
0.345247 2652
 
2.0%
0.318646 2569
 
2.0%
0.298734 2411
 
1.9%
0.611431 2381
 
1.8%
0.363547 2136
 
1.7%
Other values (345) 98402
76.0%
ValueCountFrequency (%)
0.000228 145
0.1%
0.070001 1
 
< 0.1%
0.073554 1
 
< 0.1%
0.074468 2
 
< 0.1%
0.077272 1
 
< 0.1%
0.078228 3
 
< 0.1%
0.093921 2
 
< 0.1%
0.096216 1
 
< 0.1%
0.09856 12
 
< 0.1%
0.09975 1
 
< 0.1%
ValueCountFrequency (%)
0.988494 8
 
< 0.1%
0.985419 1
 
< 0.1%
0.982479 3
 
< 0.1%
0.982248 2
 
< 0.1%
0.948826 3
 
< 0.1%
0.9441 1
 
< 0.1%
0.942676 2
 
< 0.1%
0.941218 8
 
< 0.1%
0.938965 52
< 0.1%
0.936632 2
 
< 0.1%

cont14
Real number (ℝ)

Distinct16673
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49567829
Minimum0.178568
Maximum0.844814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.4 KiB
2023-09-24T16:01:31.362386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.178568
5-th percentile0.214885
Q10.294772
median0.406198
Q30.724745
95-th percentile0.823271
Maximum0.844814
Range0.666246
Interquartile range (IQR)0.429973

Descriptive statistics

Standard deviation0.22255203
Coefficient of variation (CV)0.44898481
Kurtosis-1.5290359
Mean0.49567829
Median Absolute Deviation (MAD)0.174531
Skewness0.25299663
Sum64163.572
Variance0.049529404
MonotonicityNot monotonic
2023-09-24T16:01:31.622530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22944 120
 
0.1%
0.297706 114
 
0.1%
0.283811 106
 
0.1%
0.740079 100
 
0.1%
0.708475 98
 
0.1%
0.29791 89
 
0.1%
0.295676 85
 
0.1%
0.237754 83
 
0.1%
0.229429 83
 
0.1%
0.822889 82
 
0.1%
Other values (16663) 128486
99.3%
ValueCountFrequency (%)
0.178568 1
< 0.1%
0.180426 1
< 0.1%
0.18045 1
< 0.1%
0.180479 1
< 0.1%
0.180512 2
< 0.1%
0.180546 1
< 0.1%
0.180608 2
< 0.1%
0.180704 1
< 0.1%
0.180776 1
< 0.1%
0.180815 1
< 0.1%
ValueCountFrequency (%)
0.844814 3
 
< 0.1%
0.844729 10
< 0.1%
0.844448 3
 
< 0.1%
0.84444 2
 
< 0.1%
0.844402 1
 
< 0.1%
0.844389 1
 
< 0.1%
0.844312 1
 
< 0.1%
0.844303 4
 
< 0.1%
0.844214 1
 
< 0.1%
0.844137 1
 
< 0.1%

Interactions

2023-09-24T16:00:28.333003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:50.872384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:53.609066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:56.351929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:58.802211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:01.330086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:04.222674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:06.776801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:09.288391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:12.062708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:14.738948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:17.378547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:20.337318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:23.005037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:25.721526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:28.526169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:51.034567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:53.805594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:56.512985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:58.965766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:01.509512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:04.395204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:06.941979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:09.456117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:12.231388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:14.916144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:17.571114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:20.526059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:23.193778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:25.903658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:28.696537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:51.203708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:54.009861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:56.681173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:59.126021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:01.702920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:04.576193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:07.104887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:09.614950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:12.401379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:15.079737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:17.745012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:20.699216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:23.380179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:26.077637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:28.867259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:51.365200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:54.190788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:56.835668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:59.285249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:01.869282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:04.750009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:07.262368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:09.772311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:12.573025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:15.251089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:17.912816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:20.872799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:23.551010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:26.246976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:29.041551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:51.522465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:54.350012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:56.993407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:59.438074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:02.347575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:04.915258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:07.424568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:09.934372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:12.773142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:15.417253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:18.082460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:21.040781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:23.732274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:26.409814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:29.476270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:51.711188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:54.513358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:57.157299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:59.607109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:02.532677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:05.087191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:07.600155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:10.104544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:12.994322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:15.612804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:18.277719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:21.228397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:23.909510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:26.589543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:29.660653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:51.911531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:54.686242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:57.323528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:59.769979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:02.701198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:05.259173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:07.767751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:10.267818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:13.179149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:15.794750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:18.461712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:21.407766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:24.093765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:26.775379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:29.829511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:52.072721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:54.879322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:57.481703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:59.929539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:02.868702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:05.426317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:07.943440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:10.431891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:13.345151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:15.962988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:18.630212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:21.581084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:24.271171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:26.938816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:30.004664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:52.244301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:55.100445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:57.638768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:00.089428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:03.035772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:05.590026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:08.100497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:10.876335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:13.510036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:16.136342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:18.792888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:21.744909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:24.456159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:27.108459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:30.189730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:52.421053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:55.304936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:57.804902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:00.268363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:03.227654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:05.771546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:08.278262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:11.056244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:13.702835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:16.327120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:18.991787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:21.924555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:24.656722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:27.291101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:30.380346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:52.582491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:55.466278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:57.976815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:00.420775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:03.395929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:05.947479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:08.457910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:11.211787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:13.874421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:16.509143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:19.184740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:22.098106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:24.832396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:27.462963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:30.552901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:52.754625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:55.626052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:58.132451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:00.588486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:03.561875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:06.110715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:08.633534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:11.383575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:14.038759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:16.680147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:19.371896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:22.281427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:25.008360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:27.633311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:30.725078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:53.116273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:55.781550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:58.303133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:00.746588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:03.726310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:06.277867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:08.793501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:11.538159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:14.227288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:16.851328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:19.540793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:22.458045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:25.186163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:27.803307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:30.901365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:53.275441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:55.954743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:58.462420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:00.973482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:03.889378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:06.439553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:08.950682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:11.708329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:14.396431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:17.037125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:19.991524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:22.642098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:25.363479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:27.983015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:31.074439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:53.434244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:56.159782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T15:59:58.640602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:01.153689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:04.055044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:06.603505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:09.111038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:11.881454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:14.564353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:17.208746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:20.161714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:22.817573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:25.547232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T16:00:28.152973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-24T16:01:32.017176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idcont1cont2cont3cont4cont5cont6cont7cont8cont9cont10cont11cont12cont13cont14cat1cat2cat3cat4cat5cat6cat7cat8cat9cat10cat11cat12cat13cat14cat15cat16cat17cat18cat19cat20cat21cat22cat23cat24cat25cat26cat27cat28cat29cat30cat31cat32cat33cat34cat35cat36cat37cat38cat39cat40cat41cat42cat43cat44cat45cat46cat47cat48cat49cat50cat51cat52cat53cat54cat55cat56cat57cat58cat59cat60cat61cat62cat63cat64cat65cat66cat67cat68cat69cat70cat71cat72cat73cat74cat75cat76cat77cat78cat79cat80cat81cat82cat83cat84cat85cat86cat87cat88cat89cat90cat91cat92cat93cat94cat95cat96cat97cat98cat99cat100cat101cat102cat103cat104cat105cat106cat107cat108cat111cat114cat115
id1.0000.0010.0050.003-0.003-0.0050.0020.0020.0020.0010.0020.0020.0020.0050.0020.0000.0000.0000.0000.0120.0000.0020.0000.0050.0000.0000.0040.0000.0040.0070.0040.0000.0000.0060.0000.0000.0000.0040.0000.0000.0050.0040.0030.0090.0060.0050.0110.0000.0060.0040.0090.0070.0080.0060.0000.0030.0000.0000.0100.0030.0000.0070.0050.0000.0000.0020.0040.0000.0000.0090.0000.0030.0000.0040.0060.0000.0000.0000.0080.0000.0000.0090.0020.0000.0000.0030.0040.0000.0000.0060.0060.0060.0000.0020.0000.0070.0020.0040.0000.0070.0000.0020.0020.0000.0000.0020.0000.0040.0070.0040.0020.0000.0000.0030.0000.0050.0040.0060.0000.0010.0030.0040.0000.1530.0040.002
cont10.0011.000-0.074-0.3840.360-0.0090.7140.4600.2880.9080.7840.6080.6200.4630.0420.0410.0660.0470.0200.0500.0310.0270.0300.0600.0310.0320.0640.0270.0120.0270.0450.0310.0060.0140.0060.0040.0000.0380.0280.0090.0180.0370.0240.0110.0100.0060.1900.0120.0000.0830.0230.0720.0130.0080.0210.0250.2420.0130.0480.0130.0100.0060.0940.0050.0170.0200.0140.0430.0170.0660.0750.0280.0020.0050.0000.0180.0040.0040.0030.0150.0300.0220.0060.0040.0270.2250.0470.0120.0000.0690.0130.0040.0090.0320.0440.0160.0230.0190.0350.0050.4130.0300.4340.0100.0210.0340.0320.0100.1410.3000.1850.5320.4860.3130.0390.0230.0130.0170.3830.1580.3690.3650.4010.0070.0200.268
cont20.005-0.0741.0000.4630.0360.1730.0110.0460.119-0.0100.0790.0920.0830.080-0.0600.0290.0480.0270.0090.0160.0320.0470.0190.0430.0260.0410.0410.0360.0100.0040.0260.0130.0190.0080.0040.0040.0000.0110.0160.0090.0080.0060.0150.0110.0120.0110.0260.0050.0000.0000.0160.0160.0110.0130.0190.0210.0250.0190.0240.0140.0070.0060.0070.0000.0370.0150.0000.0070.0120.0020.0000.0650.0060.0060.0120.0050.0000.0120.0000.0090.0160.0050.0000.0090.0020.0740.0240.0140.0000.3340.0130.0040.0040.0290.0290.0120.0270.0640.0150.0050.0580.0220.0440.0180.0120.0120.0070.0180.3330.0430.0670.0650.0680.0640.0190.0380.0180.0090.1160.0780.0790.1110.0600.0000.0330.104
cont30.003-0.3840.4631.000-0.3500.081-0.3130.051-0.190-0.328-0.2880.009-0.005-0.375-0.0520.0280.0310.0290.0110.0210.0250.0360.0210.0290.0220.0220.0390.0200.0120.0230.0240.0180.0170.0210.0000.0100.0000.0180.0260.0140.0190.0170.0150.0150.0220.0090.1190.0090.0000.0490.0140.0340.0170.0150.0090.0230.1340.0230.0230.0220.0100.0110.0480.0150.0340.0210.0190.0230.0040.0390.0360.0460.0120.0110.0000.0060.0000.0000.0020.0050.0270.0100.0040.0000.0150.6740.0410.0110.0070.1720.0220.0080.0130.0200.0260.0200.0180.0350.0300.0090.2490.0190.2520.0140.0130.0220.0220.0080.2920.1750.1850.2140.2810.1660.0260.0190.0100.0080.3510.2030.1740.1640.2290.0070.0150.220
cont4-0.0030.3600.036-0.3501.0000.1860.207-0.0610.5530.3290.2820.1070.1170.2120.0250.0180.0370.0240.0130.0290.0340.0290.0150.0350.0230.0250.0460.0180.0090.0090.0190.0130.0040.0170.0000.0000.0000.0180.0170.0150.0070.0160.0190.0080.0020.0120.0960.0090.0030.0370.0110.0340.0190.0150.0210.0200.1140.0080.0250.0170.0070.0080.0420.0130.0370.0220.0080.0290.0070.0280.0330.0340.0020.0040.0060.0070.0040.0050.0100.0120.0150.0090.0070.0000.0110.0980.0240.0140.0110.0740.0130.0080.0060.0220.0260.0180.0140.0170.0330.0070.2280.0170.1980.0100.0120.0260.0270.0070.1720.1670.1690.2190.2490.1600.0300.0170.0060.0100.2690.1070.1780.1640.2380.0070.0140.182
cont5-0.005-0.0090.1730.0810.1861.000-0.160-0.2380.032-0.097-0.067-0.174-0.167-0.024-0.0350.0150.0200.0190.0090.0130.0160.0170.0070.0190.0160.0110.0140.0060.0000.0000.0190.0030.0120.0120.0060.0000.0000.0100.0160.0100.0140.0050.0040.0070.0070.0080.0310.0010.0060.0160.0040.0160.0140.0010.0060.0120.0420.0080.0200.0000.0100.0000.0200.0090.0220.0080.0100.0110.0000.0140.0100.0210.0000.0030.0060.0070.0000.0060.0030.0030.0050.0060.0000.0030.0080.1160.0210.0070.0000.0820.0060.0010.0010.0070.0090.0090.0080.0180.0110.0020.1390.0120.0840.0050.0100.0100.0090.0000.1090.1360.0680.1230.1400.1050.0140.0120.0080.0000.1370.0860.1400.1290.1580.0040.0130.146
cont60.0020.7140.011-0.3130.207-0.1601.0000.7740.4110.7990.8730.8070.8140.7330.0250.0430.0710.0310.0240.0380.0550.0140.0270.0610.0280.0460.0900.0390.0100.0190.0280.0290.0050.0170.0070.0000.0030.0380.0210.0220.0050.0240.0190.0090.0020.0230.1210.0180.0060.0630.0200.0620.0130.0110.0220.0230.1590.0180.0410.0150.0130.0110.0690.0110.0330.0350.0200.0460.0160.0460.0490.0180.0060.0000.0030.0090.0070.0000.0020.0130.0330.0160.0060.0070.0190.2830.0520.0050.0000.0550.0170.0070.0070.0290.0420.0180.0240.0270.0320.0000.4920.0240.2740.0060.0140.0310.0300.0110.1430.3280.2150.5940.4550.3010.0400.0270.0120.0140.5010.2020.5420.3260.4480.0090.0280.356
cont70.0020.4600.0460.051-0.061-0.2380.7741.0000.2320.5590.6070.8050.8030.3300.0120.0470.0580.0280.0270.0180.0450.0100.0200.0510.0300.0450.0750.0340.0130.0020.0330.0240.0000.0180.0000.0000.0000.0370.0190.0250.0140.0170.0170.0150.0220.0210.0500.0080.0030.0280.0220.0360.0120.0100.0190.0190.0680.0240.0320.0250.0070.0040.0320.0000.0370.0200.0130.0270.0090.0200.0210.0090.0060.0000.0090.0030.0000.0110.0140.0120.0230.0090.0080.0030.0040.1720.0660.0120.0000.0480.0040.0000.0040.0250.0340.0250.0240.0330.0250.0030.3200.0230.1270.0070.0120.0220.0190.0170.1340.2240.0840.4670.2880.2140.0320.0230.0110.0050.3160.2280.4010.2510.4320.0090.0230.252
cont80.0020.2880.119-0.1900.5530.0320.4110.2321.0000.3740.2940.2960.3060.4410.0350.0210.0460.0200.0100.0260.0390.0140.0200.0410.0240.0290.0510.0270.0050.0140.0200.0150.0110.0000.0000.0000.0000.0220.0100.0060.0040.0190.0090.0060.0000.0140.0990.0050.0040.0420.0130.0410.0080.0090.0080.0080.1200.0080.0230.0100.0060.0000.0510.0020.0290.0200.0100.0410.0120.0330.0370.0170.0070.0050.0000.0000.0090.0070.0150.0000.0200.0130.0110.0080.0120.1230.0240.0110.0030.0590.0120.0090.0080.0130.0200.0110.0150.0150.0250.0090.3030.0170.2250.0060.0090.0230.0230.0010.1670.2440.1110.1610.2890.2060.0260.0190.0080.0100.2180.0940.1730.1990.2190.0050.0160.225
cont90.0010.908-0.010-0.3280.329-0.0970.7990.5590.3741.0000.7950.6750.6850.5490.0480.0390.0670.0430.0220.0480.0370.0210.0260.0590.0240.0370.0720.0310.0110.0280.0400.0300.0110.0140.0000.0060.0000.0370.0220.0130.0200.0390.0180.0090.0080.0100.1940.0100.0000.0850.0220.0720.0110.0080.0170.0210.2410.0160.0480.0130.0090.0070.0970.0060.0240.0270.0130.0490.0150.0680.0750.0210.0040.0030.0000.0170.0090.0000.0080.0150.0260.0220.0090.0000.0330.1920.0470.0130.0000.0710.0130.0050.0130.0260.0390.0160.0210.0220.0320.0050.5020.0270.4570.0100.0190.0330.0310.0110.1590.3450.1610.5990.5440.3330.0380.0240.0110.0180.3800.1760.3790.3750.4890.0070.0260.307
cont100.0020.7840.079-0.2880.282-0.0670.8730.6070.2940.7951.0000.7370.7450.6420.0280.0460.0760.0370.0210.0480.0440.0160.0320.0670.0380.0500.0840.0400.0080.0290.0380.0290.0160.0160.0100.0000.0020.0400.0220.0190.0100.0280.0230.0100.0090.0110.1310.0140.0000.0640.0190.0700.0200.0060.0240.0230.1750.0150.0540.0200.0130.0060.0730.0000.0290.0250.0110.0470.0180.0450.0470.0210.0000.0000.0040.0080.0000.0140.0190.0190.0320.0150.0060.0140.0220.1480.0470.0070.0000.0800.0130.0080.0060.0310.0450.0160.0210.0290.0330.0060.4690.0280.3000.0100.0160.0310.0290.0100.1480.3120.1660.6050.4630.2990.0390.0290.0140.0160.4160.2110.4270.3460.4500.0070.0260.343
cont110.0020.6080.0920.0090.107-0.1740.8070.8050.2960.6750.7371.0000.9950.4380.0360.0440.0620.0270.0200.0340.0420.0120.0150.0500.0230.0430.0740.0320.0100.0070.0290.0220.0100.0200.0030.0030.0040.0330.0120.0220.0130.0220.0140.0140.0180.0120.0740.0090.0000.0380.0150.0490.0050.0140.0170.0150.0990.0270.0300.0240.0050.0030.0400.0040.0330.0120.0130.0250.0070.0250.0310.0170.0000.0000.0100.0000.0050.0120.0030.0110.0150.0030.0020.0000.0080.1500.0620.0090.0030.0840.0050.0050.0060.0210.0310.0210.0250.0330.0270.0040.2850.0230.1710.0070.0120.0290.0270.0110.1330.1810.1330.3710.2640.1980.0370.0240.0060.0110.4760.3540.4110.2170.2520.0070.0250.236
cont120.0020.6200.083-0.0050.117-0.1670.8140.8030.3060.6850.7450.9951.0000.4480.0390.0430.0620.0280.0180.0360.0440.0080.0100.0510.0230.0410.0720.0300.0080.0070.0310.0230.0080.0210.0060.0000.0050.0330.0080.0220.0150.0230.0100.0120.0190.0120.0840.0110.0000.0410.0140.0510.0040.0120.0140.0120.1110.0270.0270.0240.0000.0040.0440.0000.0340.0090.0150.0270.0110.0300.0380.0140.0000.0040.0070.0000.0000.0130.0010.0120.0120.0040.0000.0000.0120.1660.0640.0100.0050.0820.0030.0000.0050.0220.0310.0200.0260.0340.0280.0040.2850.0230.1870.0050.0130.0310.0290.0110.1370.1830.1580.3850.2670.2070.0370.0230.0030.0120.4850.3520.4190.2150.2540.0070.0250.233
cont130.0050.4630.080-0.3750.212-0.0240.7330.3300.4410.5490.6420.4380.4481.0000.0340.0180.0610.0290.0230.0400.0690.0170.0370.0490.0260.0450.0770.0430.0170.0270.0260.0210.0110.0170.0000.0000.0020.0300.0270.0190.0320.0270.0210.0200.0200.0260.1390.0200.0130.0690.0210.0590.0090.0230.0300.0310.1820.0170.0530.0250.0120.0100.0750.0190.0460.0500.0230.0610.0210.0510.0540.0160.0000.0050.0030.0100.0000.0000.0000.0150.0390.0270.0060.0000.0230.2410.0430.0150.0110.0940.0230.0090.0090.0250.0370.0180.0110.0140.0280.0070.7290.0210.3170.0050.0130.0300.0270.0080.1740.5080.1950.4710.6090.4930.0370.0250.0160.0160.3620.2150.3220.3800.5870.0120.0320.510
cont140.0020.042-0.060-0.0520.025-0.0350.0250.0120.0350.0480.0280.0360.0390.0341.0000.0490.0430.0310.0250.0260.0500.0190.0370.0400.0440.0290.0410.0300.0170.0000.0310.0190.0120.0000.0000.0050.0000.0290.0190.0260.0260.0140.0260.0140.0080.0240.0260.0000.0030.0140.0370.0060.0270.0180.0330.0250.0280.0070.0290.0170.0060.0000.0110.0060.0540.0110.0070.0250.0170.0080.0120.0190.0000.0000.0000.0040.0080.0110.0000.0180.0360.0030.0100.0110.0040.0400.1790.0590.0110.0590.0130.0020.0040.0320.0390.0230.0400.0380.0790.0060.0540.0250.0580.0080.0140.0620.0500.0120.0460.0450.0420.0500.0750.0360.0620.0190.0160.0130.0570.0260.0340.0370.0590.0110.0200.040
cat10.0000.0410.0290.0280.0180.0150.0430.0470.0210.0390.0460.0440.0430.0180.0491.0000.1670.1030.0260.0040.0830.0710.0870.1580.1400.0980.1480.0950.0440.0050.0820.0370.0300.0420.0120.0140.0030.0770.0570.0240.0040.0410.0730.0500.0240.0290.0010.0180.0110.0000.0650.0580.0000.0190.0820.0610.0100.0230.0470.0460.0170.0050.0010.0240.0750.0080.0170.0180.0080.0080.0030.0640.0150.0170.0130.0240.0000.0090.0020.0380.0800.0170.0110.0000.0000.0000.2050.3060.0090.0190.0460.0030.0250.1750.2160.0970.0970.0280.0670.0340.0220.1470.0000.0710.1030.1300.0160.0350.0280.0090.0090.0430.0230.0280.5200.1880.0870.0570.0390.0350.0470.0330.0390.0660.0990.027
cat20.0000.0660.0480.0310.0370.0200.0710.0580.0460.0670.0760.0620.0620.0610.0430.1671.0000.1400.0370.0600.4360.0700.0030.9310.4780.3900.4800.3880.1300.0140.1920.0400.0130.0470.0260.0220.0060.2320.0370.0800.1570.2360.0600.0760.0870.0510.0490.0240.0300.0110.2350.2430.0880.0380.0430.0310.0590.0910.1880.0840.0180.0330.0180.1570.4050.0530.1510.1970.1070.0140.0190.0760.0100.0000.0140.0130.0020.0000.0000.0000.0030.0100.0000.0190.0050.0140.1360.1370.0130.0290.0550.0510.0530.1750.2180.0580.0830.0090.0620.0560.0550.2290.0370.0700.1400.1180.0440.0280.0260.0360.0330.0610.0590.0580.4741.0000.0130.1090.0590.0370.0620.0440.0660.0810.4360.048
cat30.0000.0470.0270.0290.0240.0190.0310.0280.0200.0430.0370.0270.0280.0290.0310.1030.1401.0000.0170.0240.0510.0730.1220.1430.2120.1250.1970.1250.0260.0000.7850.3500.3000.4060.1240.1900.0600.0330.0000.0300.0470.0510.0060.0040.0100.0270.0080.0090.0080.0000.0310.0540.0350.0270.0080.0000.0080.0150.0610.0000.0070.0080.0030.0190.0410.0110.0120.0040.0050.0090.0030.0660.0210.0180.0260.0160.0190.0020.0000.0590.1190.0040.0000.0000.0000.0160.0770.0810.0110.0160.0140.0140.0230.2440.2990.0350.0370.0180.0650.0360.0110.5130.0180.0741.0000.1000.0110.0090.0110.0080.0150.0420.0210.0260.1860.2350.1260.0390.0300.0240.0370.0350.0350.0320.0570.022
cat40.0000.0200.0090.0110.0130.0090.0240.0270.0100.0220.0210.0200.0180.0230.0250.0260.0370.0171.0000.2850.1330.0260.0000.0370.0170.1940.0040.0940.0120.0000.0050.0000.0040.0260.0100.0000.0000.6470.2720.4830.3710.5080.2940.2080.2020.2490.1200.1050.0790.0470.1480.1900.1750.0860.0700.0830.0440.0620.1760.0490.0340.0320.0190.0830.1290.0210.0980.0720.0300.0050.0000.0220.0000.0020.0000.0140.0040.0000.0000.0180.0110.0000.0030.0140.0060.0000.0600.0560.0000.0100.0540.0420.0450.0740.1240.1530.0310.0270.0720.0450.0220.0560.0130.0260.0200.0760.0440.0060.0170.0090.0110.0190.0300.0240.2130.0760.0040.2910.0230.0090.0170.0210.0351.0000.1350.017
cat50.0120.0500.0160.0210.0290.0130.0380.0180.0260.0480.0480.0340.0360.0400.0260.0040.0600.0240.2851.0000.1690.0280.0160.0690.0540.1100.0210.1780.0240.0000.0110.0020.0100.0170.0050.0130.0000.1560.0820.1800.1490.1860.0700.0470.0620.0950.0320.0370.0270.0170.6270.5090.4630.2290.2940.2720.1350.2060.4180.2120.0930.0860.0520.1140.1660.0250.0620.0890.0450.0050.0000.0260.0020.0000.0000.0210.0000.0000.0000.0190.0090.0000.0080.0150.0010.0020.0170.0030.0360.0010.0590.0450.0490.0700.0880.1230.0220.0270.0630.0480.0150.0480.0400.0280.0270.1140.0480.0010.0040.0150.0180.0360.0430.0150.2090.0780.0181.0000.0430.0180.0350.0270.0430.2890.1700.020
cat60.0000.0310.0320.0250.0340.0160.0550.0450.0390.0370.0440.0420.0440.0690.0500.0830.4360.0510.1330.1691.0000.0460.0560.4070.2260.1910.2270.1870.0490.0050.1040.0340.0270.1080.0080.0100.0000.2380.1010.1560.0650.1600.1080.0160.0180.0730.0190.0330.0080.0100.2420.1160.1610.0750.1140.1070.0070.0000.0960.0050.0310.0090.0110.3450.9260.1210.3360.4540.2380.0430.0450.0550.0070.0140.0000.0240.0060.0070.0010.0070.0600.0050.0140.0160.0120.0000.0220.0610.0340.0110.0430.0410.0450.1360.1730.1350.0200.0050.0320.0480.0650.1530.0220.0470.0510.1350.0870.0200.0400.0440.0200.0280.0650.0660.5220.4370.0560.1840.0390.0230.0310.0570.0690.1521.0000.063
cat70.0020.0270.0470.0360.0290.0170.0140.0100.0140.0210.0160.0120.0080.0170.0190.0710.0700.0730.0260.0280.0461.0000.0580.0690.0900.0690.0910.0720.0790.0020.0700.0480.0180.0060.0270.0020.0300.0460.0310.0120.0020.0190.0420.0170.0110.0030.0050.0210.0060.0000.0440.0140.0120.0090.0450.0300.0080.0150.0020.0250.0160.0000.0000.0130.0430.0020.0140.0090.0070.0000.0000.8090.2250.2580.2910.4120.0810.1220.0850.0330.0470.0140.0000.0050.0000.0000.0780.0560.0180.0210.0590.0090.0170.1800.1990.0770.0360.0190.0510.0220.0140.1240.0051.0000.0830.0720.0150.0100.0370.0120.0050.0230.0120.0320.1150.1340.0580.0310.0160.0240.0100.0190.0280.0340.0480.014
cat80.0000.0300.0190.0210.0150.0070.0270.0200.0200.0260.0320.0150.0100.0370.0370.0870.0030.1220.0000.0160.0560.0581.0000.0000.0070.0160.0190.0190.0410.0000.0550.0850.0960.0630.0110.0480.0060.0080.0700.0080.0140.0230.0950.0720.0440.0000.0090.0130.0080.0110.0020.0270.0060.0000.1230.0900.0000.0540.0210.0830.0090.0120.0000.0210.0550.0100.0210.0000.0080.0320.0140.0210.0130.0130.0340.0590.0150.0070.0170.4420.8630.2340.1170.1580.0490.0210.0180.0810.0470.0130.0410.0140.0360.1470.2120.0280.0140.0110.0360.0200.0370.1400.0050.0590.1280.2200.1770.0420.0160.0190.0150.0180.0330.0480.2430.0381.0000.0530.0240.0280.0200.0270.0460.0540.0630.038
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cat130.0000.0270.0360.0200.0180.0060.0390.0340.0270.0310.0400.0320.0300.0430.0300.0950.3880.1250.0940.1780.1870.0720.0190.3450.3480.3060.3771.0000.0590.0030.1580.0450.0220.0220.0290.0110.0070.0000.0380.0700.0770.1050.0310.0380.0420.0450.0220.0160.0150.0060.3550.0910.0280.0000.0800.0740.0180.0360.0680.0330.0000.0100.0040.0630.1740.0220.0530.0850.0430.0050.0050.0820.0100.0000.0110.0050.0000.0040.0030.0000.0230.0000.0030.0040.0000.0070.1100.0760.0110.0260.0240.0200.0220.1540.1920.0690.0480.0220.0410.0230.0360.2030.0160.0720.1270.0620.0420.0110.0230.0290.0220.0260.0360.0410.2430.6700.0190.1990.0290.0170.0310.0320.0410.1030.1880.037
cat140.0040.0120.0100.0120.0090.0000.0100.0130.0050.0110.0080.0100.0080.0170.0170.0440.1300.0260.0120.0240.0490.0790.0410.0550.0740.0570.1180.0591.0000.0110.0280.0200.0090.0060.0140.0000.0000.0320.0070.0140.0080.0220.0170.0000.0020.0310.0000.0280.0020.0030.0440.0160.0280.0530.0210.0150.0000.0050.0000.0000.0370.0110.0000.0180.0500.0070.0160.0180.0120.0000.0000.0900.0220.0040.0000.0080.0060.0000.0160.0100.0030.1330.0260.0000.0000.0000.0300.0290.0190.0100.6150.0060.0290.0720.0900.0400.0170.0060.0130.0140.0000.0580.0080.0810.0310.0630.0420.0030.0100.0040.0000.0050.0090.0100.1330.3730.0420.0410.0130.0080.0120.0170.0170.0190.0480.010
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cat1050.0010.1580.0780.2030.1070.0860.2020.2280.0940.1760.2110.3540.3520.2150.0260.0350.0370.0240.0090.0180.0230.0240.0280.0330.0190.0240.0320.0170.0080.0000.0210.0380.0000.0340.0000.0000.0000.0180.0250.0180.0140.0150.0180.0250.0290.0070.0340.0000.0000.0080.0150.0270.0100.0000.0250.0300.0370.0370.0250.0410.0050.0000.0060.0110.0240.0140.0120.0150.0000.0000.0100.0260.0100.0000.0090.0070.0000.0060.0000.0070.0330.0000.0000.0000.0000.1400.0430.0090.0050.0970.0130.0080.0000.0160.0200.0190.0180.0260.0150.0000.2700.0170.0800.0060.0160.0160.0160.0120.1350.1550.1350.2550.1720.1910.0170.0110.0140.0080.2121.0000.2240.2450.2520.0000.0100.333
cat1060.0030.3690.0790.1740.1780.1400.5420.4010.1730.3790.4270.4110.4190.3220.0340.0470.0620.0370.0170.0350.0310.0100.0200.0560.0230.0390.0720.0310.0120.0140.0380.0230.0000.0190.0000.0000.0000.0350.0130.0190.0000.0200.0200.0100.0120.0150.0850.0090.0000.0460.0200.0520.0090.0020.0140.0110.1150.0250.0300.0260.0050.0000.0500.0120.0190.0210.0140.0240.0140.0310.0420.0140.0000.0000.0000.0080.0000.0000.0000.0150.0210.0150.0030.0000.0110.2980.0630.0060.0000.0950.0120.0050.0040.0260.0360.0200.0260.0260.0280.0060.2600.0280.1820.0000.0150.0310.0280.0100.1140.1730.1970.6800.2470.1670.0300.0170.0090.0110.4140.2241.0000.3970.2920.0120.0070.308
cat1070.0040.3650.1110.1640.1640.1290.3260.2510.1990.3750.3460.2170.2150.3800.0370.0330.0440.0350.0210.0270.0570.0190.0270.0390.0310.0340.0530.0320.0170.0110.0400.0190.0000.0240.0000.0080.0000.0220.0150.0140.0080.0180.0150.0040.0120.0200.0960.0080.0080.0400.0180.0320.0170.0210.0200.0200.1220.0120.0330.0210.0000.0090.0550.0150.0460.0530.0150.0510.0170.0330.0350.0270.0000.0000.0000.0110.0000.0000.0000.0150.0270.0270.0070.0000.0100.1350.0230.0190.0080.0940.0190.0060.0110.0190.0240.0160.0170.0140.0240.0050.5420.0230.2460.0000.0180.0160.0110.0090.1510.4200.0820.5630.5600.2740.0200.0140.0120.0080.3060.2450.3971.0000.4200.0000.0140.541
cat1080.0000.4010.0600.2290.2380.1580.4480.4320.2190.4890.4500.2520.2540.5870.0590.0390.0660.0350.0350.0430.0690.0280.0460.0570.0370.0470.0820.0410.0170.0340.0380.0270.0140.0310.0040.0000.0000.0380.0340.0260.0520.0370.0260.0260.0260.0260.2250.0190.0050.0870.0320.0650.0180.0220.0350.0360.2710.0230.0710.0280.0080.0100.1010.0150.0520.0580.0230.0670.0220.0760.0810.0280.0060.0000.0070.0120.0000.0080.0000.0180.0470.0320.0130.0030.0340.1840.0590.0220.0110.0730.0220.0080.0130.0300.0410.0300.0190.0300.0360.0080.8830.0270.5360.0110.0170.0330.0300.0150.2080.7170.1260.5480.9940.6360.0380.0250.0190.0150.2960.2520.2920.4201.0000.0110.0330.481
cat1110.1530.0070.0000.0070.0070.0040.0090.0090.0050.0070.0070.0070.0070.0120.0110.0660.0810.0321.0000.2890.1520.0340.0540.0830.0620.2080.0600.1030.0190.0000.0340.0000.0090.0250.0070.0000.0000.6600.4970.6900.5840.5400.4780.3830.4080.4650.1740.2950.2910.1870.1570.1900.1760.0880.0730.0840.0440.0660.1760.0540.0330.0290.0300.1140.1470.0190.0990.0830.0320.0020.0000.0270.0090.0230.0090.0350.0000.0000.0000.0440.0380.0380.1160.1230.0020.0170.0820.0440.0270.0000.1050.0200.0620.0650.0990.1320.0200.0140.0410.0330.0090.0410.0070.0110.0130.0330.0210.0000.0000.0080.0000.0030.0160.0010.0690.1790.0580.0840.0160.0000.0120.0000.0111.0000.0860.000
cat1140.0040.0200.0330.0150.0140.0130.0280.0230.0160.0260.0260.0250.0250.0320.0200.0990.4360.0570.1350.1701.0000.0480.0630.4070.2260.1910.2270.1880.0480.0000.1040.0350.0280.1220.0000.0720.0000.2380.1000.1570.0670.1650.1080.0320.0320.0720.0480.0320.0120.0020.2420.1210.1620.0740.1140.1070.0340.0240.1000.0230.0300.0130.0050.6070.9280.3040.5960.7160.4900.2650.2460.0550.0190.0330.0070.0220.0170.0080.0130.0360.0630.0000.0110.0230.0540.0000.0800.0450.0220.0110.0920.0210.0260.0860.1140.0830.0480.0410.0270.0290.0420.0910.0210.0210.0350.0610.0410.0410.0230.0270.0070.0290.0390.0180.1440.1110.0450.0520.0120.0100.0070.0140.0330.0861.0000.017
cat1150.0020.2680.1040.2200.1820.1460.3560.2520.2250.3070.3430.2360.2330.5100.0400.0270.0480.0220.0170.0200.0630.0140.0380.0390.0320.0420.0670.0370.0100.0180.0260.0190.0060.0220.0000.0160.0000.0200.0260.0210.0250.0170.0150.0240.0240.0190.1000.0120.0060.0440.0200.0340.0150.0220.0260.0310.1260.0130.0410.0230.0090.0130.0480.0200.0420.0520.0220.0590.0200.0360.0380.0180.0080.0000.0000.0130.0000.0000.0000.0080.0430.0340.0040.0030.0110.1280.0200.0170.0090.1090.0220.0120.0120.0220.0280.0170.0120.0120.0230.0050.6790.0200.2520.0000.0150.0200.0180.0070.1880.4950.1840.3430.5550.3630.0210.0150.0140.0070.2380.3330.3080.5410.4810.0000.0171.000

Missing values

2023-09-24T16:00:32.215296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-24T16:00:34.193474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idcat1cat2cat3cat4cat5cat6cat7cat8cat9cat10cat11cat12cat13cat14cat15cat16cat17cat18cat19cat20cat21cat22cat23cat24cat25cat26cat27cat28cat29cat30cat31cat32cat33cat34cat35cat36cat37cat38cat39cat40cat41cat42cat43cat44cat45cat46cat47cat48cat49cat50cat51cat52cat53cat54cat55cat56cat57cat58cat59cat60cat61cat62cat63cat64cat65cat66cat67cat68cat69cat70cat71cat72cat73cat74cat75cat76cat77cat78cat79cat80cat81cat82cat83cat84cat85cat86cat87cat88cat89cat90cat91cat92cat93cat94cat95cat96cat97cat98cat99cat100cat101cat102cat103cat104cat105cat106cat107cat108cat109cat110cat111cat112cat113cat114cat115cat116cont1cont2cont3cont4cont5cont6cont7cont8cont9cont10cont11cont12cont13cont14
04ABAAAAAABABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADBBDDBBCBDBAAAAADCCECDTHGAAGEILKBIBCAJAXAQHG0.3215940.2991020.2469110.4029220.2811430.4665910.3176810.612290.343650.380160.3777240.3698580.7040520.392562
16ABABAAAABAAAAAAAAAAAAABBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAABADBBDDBBCBBBAAAAADDDEAAPBDAAGGGFBBICOEGXALHK0.6347340.6208050.6543100.9466160.8364430.4824250.4437600.713300.518900.604010.6890390.6757590.4534680.208045
29ABABBABABBABBBAAAAAAAABAAAAAAAAAAAABABBAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABDBBBBBDCBBBABAAADDCEEADGQADDEJGABICSCUAEAKCK0.2908130.7370680.7111590.4127890.7185310.2123080.3257790.297580.343650.305290.2454100.2416760.2585860.297232
312AAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABABAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAADBDBDBBABDDAAAGHDDCEEDTGAADEEIKKBICRAAYAJAPDJ0.2686220.6817610.5926810.3548930.3970690.3699300.3423550.400280.332370.314800.3488670.3418720.5922640.555955
415BAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAADBBDDBBCBBBAAAAADBDEAAPAAAAFEGEBABEGAEICJHA0.5538460.2991020.2635700.6968730.3026780.3988620.3918330.236880.437310.505560.3595720.3522510.3015350.825823
517AAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAABBAABADBBDDBACBBBAAAAADBDEAAPHAACHGGDBBICLAANBCAIHY0.5949340.2459210.3979830.8495840.6433150.4073510.3905400.464770.468530.505560.6075000.5946460.2509910.283976
621BAAABBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAADBBDDBCCBDBDAABHEBCEACTFAABKEDGGBICOAAGSEQMD0.9642790.6208050.3568190.8388400.2811430.9608450.7400810.759640.983300.822490.8630520.8793470.8889440.787807
728BBAAAAAABAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABADBBDDBDCBBBAAAAADBDEAAHIFAAIFFCAABELARXAHKC0.6333890.0472970.1296460.8645860.6512460.4511150.3163130.273200.521000.505560.4150290.4813060.1999400.450597
832ABAAAAAABAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAADBBDDBBCBDBAAAAADDCEEDTLGAAFFKMKBICOAUYAQGC0.2618410.2459210.4841960.4630290.5344840.3434920.3587580.819000.321280.364580.4533340.4433740.6956500.295075
943ABAAAAAABBBABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAADBBCDBBCBDBDAAAADCCEECTLMAAFEIHGBIEGANLAODT0.5279300.4887890.3979830.7757440.2811430.3949210.2874160.923470.483200.247660.3595720.3522510.5199890.602666
idcat1cat2cat3cat4cat5cat6cat7cat8cat9cat10cat11cat12cat13cat14cat15cat16cat17cat18cat19cat20cat21cat22cat23cat24cat25cat26cat27cat28cat29cat30cat31cat32cat33cat34cat35cat36cat37cat38cat39cat40cat41cat42cat43cat44cat45cat46cat47cat48cat49cat50cat51cat52cat53cat54cat55cat56cat57cat58cat59cat60cat61cat62cat63cat64cat65cat66cat67cat68cat69cat70cat71cat72cat73cat74cat75cat76cat77cat78cat79cat80cat81cat82cat83cat84cat85cat86cat87cat88cat89cat90cat91cat92cat93cat94cat95cat96cat97cat98cat99cat100cat101cat102cat103cat104cat105cat106cat107cat108cat109cat110cat111cat112cat113cat114cat115cat116cont1cont2cont3cont4cont5cont6cont7cont8cont9cont10cont11cont12cont13cont14
129436587583BAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAADBBDDDBCBBBAAAAADDDEGAPIAABJHEEDBIACAEHAJLF0.6803880.5557820.6543100.3643020.2811430.7308040.9944210.645770.717640.625070.9096110.9016120.3543440.818373
129437587587AAABAAAAAAAAAAAAAAAAAAAAABBAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAADBBDDBBCBCBAAAGHDDEEGERNAAAGFHIFBIBTGNAXAOHK0.6829050.5557820.4841960.4327280.5344840.5978620.4758660.571870.732710.752340.5887530.5761210.7685250.601881
129438587596ABAABAAABAAABAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAADBBDDDBCBBBAAABADDAECAFJGABFGGHDBIELAJHAKGS0.2475330.6208050.6928250.2840480.2811430.4389170.7058140.776680.351270.300600.5697450.5573800.2742170.387062
129439587610AAABAAAAAAAAAAAAAAAAAAAABABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABBAAADBBDDBBCBBBAAAAADDCEDAKNAAACDLJAKELEAMAFAMCB0.1381280.6208050.5279910.2154820.3100610.1894840.2658940.259180.241800.212300.1692060.1677680.3392440.833053
129440587613AABAABAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAADBADDBDCBBBAABAADDDECAPKAAAEEHGBBICLAAMBJCLDP0.4355310.7857840.5061050.2677270.9311650.3754290.3892490.411820.422890.450170.3418130.3350360.3822520.836701
129441587617AAABAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABADBBCBBBCBBBAAAAADDDEAAPLAAAFFGFDBICLCAIHAKGS0.4584630.2459210.6543100.2840480.2811430.4389170.8159410.394550.487400.406660.5505290.5384730.2987340.345946
129442587621AAAABBABAAAAAAAAAAAAAAAAAAAAAAAAAAABBABAAAAAAAAAABABAAAAAAAAAAAAAABAAAAAAABADBBDDBBCBDBAAABHDDCEEDTFABDEFIJKBIDMAAVBJJODP0.3041430.4887890.5497700.2677270.6745290.3469480.4249680.476690.257530.268940.3244860.3522510.4900010.290576
129443587627BBAABAAABBAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADBBDDBBCBDBDAABHDDCEACTFFABKHFHGBIBPAEBMAPLY0.8996890.5557820.4841960.5945980.7947940.8089580.5115020.722990.944380.835100.9331740.9266190.8481290.808125
129444587629AAAAABABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAABAAAAAAAAAAADBBDDBACBDBAAAAADBCEEDTJABAEEJKKKEBAKAEEODJ0.3292320.3583190.4841960.3738160.3026780.3721250.3885450.317960.321280.369740.3076280.3019210.6082590.361542
129445587634ABAAAAAABABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAADBDBDBDCBBDAAAAADDDEEAPHGAADEJGBABELAAFAFALCR0.3103140.2991020.5497700.2086550.4138170.2216990.2420440.254610.313990.251830.2454100.2416760.2876820.220323